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(c) Forschungsverbund + MWW</ref> + </p> + </availability> + </publicationStmt> + <sourceDesc> + <p>Einreichung als Fachartikel in der ZfdG durch die Autor*innen</p> + </sourceDesc> + </fileDesc> + <encodingDesc> + <editorialDecl> + <p>Transformation der WORD-Vorlage nach XML/TEI-P5 durch TEI-Oxgarage und + XSLT-Skripten</p> + </editorialDecl> + <editorialDecl> + <p xml:lang="de">Lektorat des Textes durch die Redaktion in Person von + <persName>Lisa Klaffki</persName>.</p> + </editorialDecl> + <editorialDecl> + <p>Medienrechte liegen bei den Autor*innen</p> + </editorialDecl> + <editorialDecl> + <p>All links checked<date when="2021-07-22">22.07.2021</date> + </p> + </editorialDecl> + </encodingDesc> + <profileDesc> + <creation>Einreichung als Artikel der Zeitschrift für digitale + Geisteswissenschaften</creation> + <langUsage> + <language ident="en">Text in Englisch</language> + <language ident="en">Abstract in Deutsch</language> + <language ident="en">Abstract in Englisch</language> + </langUsage> + <textClass> + <keywords scheme="gnd"> + <term>Gefühl<ref target="4019702-5"/></term> + <term>Hermeneutik<ref target="4128972-9"/></term> + <term>Literaturwissenschaft<ref target="4036034-9"/></term> + <term>Netzwerkanalyse (Soziologie)<ref target="4205975-6"/></term> + <term>Textanalyse<ref target="4194196-2"/></term> + </keywords> + </textClass> + </profileDesc> + <revisionDesc> + <change when="2021-07-05" who="kahlert" n="2.0" status="published"> + <p>Es wurden folgende Änderungen vorgenommen: Inhaltliche Anpassungen, wie sie von + den Gutachten angemerkt worden sind. Austausch der Tab. 1. Aktualisierung und Ergänzung der + bibliographischen Angaben. Formale Korrekturen.</p> + </change> + </revisionDesc> + </teiHeader> + <text> + <body> + <div> + <div type="abstract"> + <argument xml:lang="de"> + <p>Emotionen sind ein wesentlicher Bestandteil fesselnder Erzählungen: + Literatur erzählt uns von Menschen mit Zielen, Wünschen, Leidenschaften + und Absichten. Die Analyse von Emotionen ist Teil des breiteren und + größeren Feldes der Sentimentanalyse und findet in der + Literaturwissenschaft zunehmend Beachtung. In der Vergangenheit wurde + die affektive Dimension der Literatur hauptsächlich im Rahmen der + literarischen Hermeneutik untersucht. Mit dem Aufkommen der Digital + Humanities (DH) als Forschungsfeld, haben jedoch einige Studien über + Emotionen im literarischen Kontext eine computergestützte Wendung + genommen. In Anbetracht der Tatsache, dass sich die DH als Feld noch im + Aufbau befindet, kann diese Forschungsrichtung als relativ neu + bezeichnet werden. In dieser Ãœbersicht bieten wir einen Ãœberblick über + die bestehende Forschung zur Emotionsanalyse in der Literatur. Die + untersuchte Forschungsliteratur befasst sich mit einer Vielzahl von + Themen, darunter die Veränderungen der emotionalen Konnotation im + Verlauf eines Texts, die Netzwerkanalyse eines literarischen Textes und + das Verstehen der Emotionalität von Texten, neben anderen Themen. + Basierend auf diesem Ãœberblick weisen wir auf eine Reihe von + verbleibenden Herausforderungen hin, die vielversprechende zukünftige + Forschungsrichtungen darstellen.</p> + </argument> + + <argument xml:lang="en"> + <p>Emotions are a crucial part of compelling narratives: literature tells us + about people with goals, desires, passions, and intentions. Emotion + analysis is part of the broader and larger field of sentiment analysis, + and receives increasing attention in literary studies. In the past, the + affective dimension of literature was mainly studied in the context of + literary hermeneutics. However, with the emergence of the research field + known as Digital Humanities (DH), some studies of emotions in a literary + context have taken a computational turn. Given the fact that DH is still + being formed as a field, this direction of research can be rendered + relatively new. In this survey, we offer an overview of the existing + body of research on emotion analysis as applied to literature. The + research under review deals with a variety of topics including tracking + dramatic changes of a plot development, network analysis of a literary + text, and understanding the emotionality of texts, among other topics. + Based on this review, we point to a set of remaining challenges that + constitute promising future research directions.</p> + </argument> + </div> + <div type="chapter"> + <head>1 Introduction and Motivation</head> + <p>This article deals with <hi rend="italic">emotion</hi> and <hi rend="italic" + >sentiment</hi> analysis in <term type="dh">computational literary + studies</term>. Following Liu,<note type="footnote"> <ref type="bibliography" target="#liu_opinions_2015">Liu 2015</ref>, + p. 2.</note> we define sentiment as a <hi rend="italic">positive</hi> or + <hi rend="italic">negative</hi> feeling underlying the opinion. + Sometimes, sentiment analysis is interpreted synonymously to opinion mining, + however strictly speaking, opinion mining is an application that makes use + of sentiment analysis and contextualizes polarity ratings in topics, aspects + and targets. Though sentiment analysis is primarily text-oriented, there are + multimodal approaches as well.<note type="footnote"> <ref type="bibliography" target="#soleymani_survey_2017">Soleymani et al. + 2017</ref>.</note> + </p> + <p>Another interpretation of the term <hi rend="italic">sentiment analysis</hi> + is as broader description of a research field, which considers affective + computing applied to textual analysis. In this sense, it also includes the + distinction into subjective or objective statements,<note type="footnote"> + <ref type="bibliography" target="#wiebe_language_2004">Wiebe et al. 2004</ref>.</note> and, more recently, the field of emotion + analysis.Defining the concept of <hi rend="italic">emotion</hi> is a + challenging task. As Scherer puts it, <quote>defining emotion is a notorious + problem</quote>.<note type="footnote"> <ref type="bibliography" target="#scherer_emotions_2005">Scherer 2005</ref>, p. 1.</note> + Indeed, different methodological and conceptual approaches to dealing with + emotions lead to different definitions. However, the majority of emotion + theorists agree that emotions involve a set of expressive, behavioral, + physiological, and phenomenological features.<note type="footnote"> + <ref type="bibliography" target="#scarantino_philosophy_2016">Scarantino 2016</ref>, p. 36.</note> In this view, an emotion can be defined + as <quote>an integrated feeling state involving physiological changes, + motor-preparedness, cognitions about action, and inner experiences that + emerges from an appraisal of the self or situation</quote>.<note + type="footnote"> <ref type="bibliography" target="#mayer_abilities_2008">Mayer et al. 2008</ref>, p. 2.</note> + </p> + <p>Similar to sentiment, emotions can be analyzed computationally. However, the + goal of emotion analysis is to recognize the emotion, rather than sentiment, + which makes it a more difficult task as differences between some emotion + classes are more subtle than those between positive and negative. </p> + <p>Although sentiment and emotion analysis are different tasks, our review of + the literature shows that the use of either term is not always consistent. + There are cases where researchers analyze only positive and negative aspects + of a text but refer to their analysis as emotion analysis. Likewise, there + are cases where researchers look into a set of subjective feelings including + emotions but call it sentiment analysis. Hence, to avoid confusion, in this + survey, we use the terms <term type="dh">emotion analysis</term> and <term + type="dh">sentiment analysis</term> interchangeably. In most cases, we + follow the terminology used by the authors of the papers we discuss (i.e., + if they call emotions sentiments, we do the same). However, our focus of + this survey is on emotion analysis, and we do not include the majority of + work that focuses on binary polarities.</p> + <p>Finally, we talk about sentiment and emotion analysis in the context of + computational literary studies. Da defines computational literary studies as + the statistical representation of patterns discovered in text mining fitted + to currently existing knowledge about literature, literary history, and + textual production.<note type="footnote"> <ref type="bibliography" target="#da_case_2019">Da 2019</ref>, p. 602.</note> + Computational literary studies are closely related to the concepts of <term + type="dh">distant reading</term> + <note type="footnote"> <ref type="bibliography" target="#moretti_graphs_2005">Moretti 2005</ref>.</note> and <term type="dh">digital + literary studies</term>,<note type="footnote"> <ref type="bibliography" target="#hoover_studies_2014">Hoover et al. + 2014</ref>.</note> each of which refers to the practice of running a textual + analysis on a computer to yield quantitative results. In this survey, we use + all of these terms interchangeably and when we refer to digital humanities + as a field, we refer to those groups of researchers whose primary objects of + study are texts. </p> + <div type="subchapter"> + <head>1.1 Scope of this Survey</head> + + <p>This survey provides an overview of work which aims at understanding or + analyzing emotions in literature. We include studies that answer a + concrete research question from the field of literary studies with + computational methods. We do only consider publications in English that + have been quality-assessed by peer review (except for few exceptions). + We exclude efforts of corpus creation and annotation, if those corpora + have not been used for a further research study to limit the scope of + this survey (though such work is clearly relevant and important) and + software development efforts if the associated papers do not aim at + contributing to answering a research question. Similarly, we do mostly + exclude reports on ongoing research efforts, if they do not contribute a + novel understanding of a research question. Our literature research + started in the field of computational linguistics with the <ref + target="https://www.aclweb.org/anthology/">ACL Anthology</ref> and + has been complemented by other research that cites such papers or is + cited by them. We exclude papers from local digital humanities + conferences. </p> + <p>The goal of this survey is to provide an overview of recent methods of + emotion and sentiment analysis as applied to a text. The survey is + directed at researchers looking for an introduction to the existing + research in the field of sentiment and emotion analysis of a (primarily, + literary) text. We do not not cover applications of emotion analysis in + the areas of digital humanities that are not focused on text. Neither do + we provide an in-depth overview of all possible applications of emotion + analysis in the computational context outside of the DH line of + research.</p> + </div> + <div type="subchapter"> + <head>1.2 Emotion Analysis and Digital Humanities</head> + + <p>Methods that apply emotion analysis can in general be categorized + into (<ref type="intern" target="#hd1">section 1</ref>) dictionary-based methods, (<ref type="intern" target="#hd5">chapter 2</ref>) feature-based + machine-learning-based, and (<ref type="intern" target="#hd9">section 3</ref>) representation-learning/deep + learning-based. Methods that apply statistical learning (<ref type="intern" target="#hd8">section 2.3</ref>) to + induce a model that takes text as input and output predictions rely + in the majority of cases (in this field) on supervised approaches – + a learning algorithm is presented with annotated data and needs to + output a model that can, as good as possible on unseen data, do such + predictions. These approaches have advantages: The learner can + exploit (long-distant) dependencies between textual units, learn + associations between semanic meaning and concepts to learn, and make + use of semantic similarities between words; even those that have not + been seen in training data. This comes at a cost – the need for + annotated data. The situation between the fields of computational + linguistics and digital humanities differs substantially in this + regard.</p> + <p>The focus in computational linguistics is to develop methods to solve + a particular task – analyze syntax, respresent semantics, or develop + well-performing classification methods, for instance for emotion + classification. Therefore, there exists a substantial body of + research on natural language processing which is essentially + agnostic to the corpus. In fact, a method is typically evaluated on + a set of different resources to prove its generalizability, and even + if a novel corpus is presented for future studies, this is compared + to existing resources. This comes with an advantage: Resources are + often built by domain experts, which are then used for further + analysis; the diversity might be limited, but is often sufficient + for model development.</p> + <p>In digital humanities, this situation differs substantially. The goal + is often not the development of a computational model that is able + to make predictions for the entirety of a field (which is of course + also not achieved in computational linguistics, but that is + sometimes claimed to be a goal). Instead, the object of research (a + particular text, a genre, an author, ...) is of higher importance. + This comes with a challenge: Annotators often need to be experts in + the particular domain, for a particular object of research.</p> + <p>That might be the reason, as we will see, that, in contrast to + research in computational linguistics, using lexicons of words + associated with the concepts of interest, receives some attention as + a methodological approach to emotion analysis. This comes at the + cost of accuracy, as such methods are (mostly) not able to interpret + the context appropriately (with some exceptions which embed + dictionaries with rules<note type="footnote"> E.g. <ref type="bibliography" target="#shaikh_interpretation_2009">Shaikh 2009</ref>. + </note>), however, it contributes the advantage of being transparent + not only with the predictions and the results, but also with the + analysis algorithm. </p> + </div> + <div type="subchapter"> + <head>1.3 Emotions and Arts</head> + + <p>Much of our daily experiences influence and are influenced by the emotions we + experience.<note type="footnote"> + <ref type="bibliography" target="#schwarz_emotion_2000">Schwarz 2000</ref>, + p. 433.</note> This experience is not limited to real events. People can + feel emotions because they are reading a novel or watching a play or a + movie.<note type="footnote"> + <ref type="bibliography" target="#johnson_emotions_2016">Johnson-Laird / + Oatley 2016</ref>; <ref type="bibliography" + target="#djikic_art_2009">Djikic et al. 2009</ref>.</note> + There is a growing + body of literature that pinpoints the importance of emotions for + literary comprehension,<note type="footnote"> + <ref type="bibliography" target="#robinson_reason_2005">Robinson 2005</ref>; + <ref type="bibliography" target="#hogan_fictions_2010">Hogan 2010</ref>; + <ref type="bibliography" target="#hogan_literature_2011">Hogan + 2011</ref>; <ref type="bibliography" target="#bal_fiction_2013">Bal / + Veltkamp 2013</ref>; <ref type="bibliography" target="#djikic_minds_2013" + >Djikic et al. 2013</ref>; <ref type="bibliography" + target="#johnson_transportation_2012">Johnson 2012</ref>; <ref + type="bibliography" target="#samur_session_2018">Samur et al. + 2018.</ref></note> + as well as + research that recognizes the deliberate choices people make with + regard to their emotional states when seeking narrative + enjoyment such as a book or a film.<note type="footnote"> + <ref type="bibliography" target="#zillmann_effect_1980">Zillmann et al. + 1980</ref>; <ref type="bibliography" target="#ross_encounter_1999">Ross + 1999</ref>; <ref type="bibliography" target="#bryant_television_1984" + >Bryant / Zillmann 1984</ref>; <ref type="bibliography" + target="#oliver_states_2008">Oliver 2008</ref>; <ref type="bibliography" + target="#mar_emotion_2011">Mar et al. 2011.</ref></note></p> + <p>The link between emotions and arts in general is a matter of + debate that dates back to the Ancient period, particularly to + Plato, who viewed passions and desires as the lowest kind of + knowledge and treated poets as undesirable members in his ideal + society.<note type="footnote"> + <ref type="bibliography" target="#plato_volumes_1969">Plato 1969</ref>.</note> In + contrast, Aristotle’s view on emotive components of poetry + expressed in his <bibl> + <title type="desc">Poetics</title> + </bibl> + <note type="footnote"> + <ref type="bibliography" target="#aristotele_poetics_1996">Aristotle 1996</ref>.</note> differed from + Plato’s in that emotions do have great importance, particularly + in the moral life of a person.<note type="footnote"> <ref type="bibliography" target="#sousa_emotion_2018">de Sousa / + Scarantino 2018</ref>.</note> In the late nineteenth century the + emotion theory of arts stepped into the spotlight of + philosophers. One of the first accounts on the topic is given by + Leo Tolstoy in 1898 in his essay <bibl> + <title type="desc">What is Art?</title> + </bibl>.<note type="footnote"> <ref type="bibliography" target="#tolstoy_art_1962">Tolstoy 1962</ref>.</note> Tolstoy + argues that art can express emotions experienced in fictitious + context and the degree to which the audience is convinced of + them defines the success of the artistic work.<note + type="footnote"> <ref type="bibliography" target="#anderson_tone_1986">Anderson / McMaster + 1986</ref>, p. 3; <ref type="bibliography" target="#hogan_fictions_2010" + >Hogan 2010</ref>, p. 187; <ref type="bibliography" + target="#piper_bestseller_2015">Piper / Jean So 2015</ref>.</note> + </p> + <p>New methods of quantitative research emerged in humanities + scholarship bringing forth the so-called <hi rend="italic" + >digital revolution</hi> + <note type="footnote"> + <ref type="bibliography" target="#lanham_word_1989">Lanham 1989</ref>.</note> and the + transformation of the field into what we know as digital + humanities.<note type="footnote"> <ref type="bibliography" target="#berry_introduction_2012">Berry 2012</ref>; + <ref type="bibliography" target="#schreibman_compainion_2016">Schreibman + et al. 2015</ref>.</note> The adoption of computational methods of + text analysis and data mining from the fields of then + fast-growing areas of computational linguistics and artificial + intelligence provided humanities scholars with new tools of text + analytics and data-driven approaches to theory formulation.<note + type="footnote"> <ref type="bibliography" target="#vanhoutte_gates_2013">Vanhoutte + 2013</ref>, p. 142; <ref type="bibliography" + target="#jockers_humanities_2016">Jockers / Underwood 2016</ref>, pp. + 292f.</note> + </p> + <p>To the best of our knowledge, the first work<note type="footnote" + > <ref type="bibliography" target="#anderson_computer_1982">Anderson / + McMaster 1982</ref>.</note> on a computer-assisted + modeling of emotions in literature appeared in 1982. Challenged + by the question of why some texts are more interesting than + others, Anderson and McMaster concluded that the + <quote>emotional tone</quote> of a story can be responsible + for the reader’s interest. The results of their study suggest + that a large-scale analysis of the <quote>emotional tone</quote> + of a collection of texts is possible with the help of a computer + program. There are two implications of this finding. First, they + suggested that by identifying emotional tones of text passages + one can model affective patterns of a given text or a collection + of texts, which in turn can be used to challenge or test + existing literary theories. Second, their approach to affect + modeling demonstrates that the stylistic properties of texts can + be defined on the basis of their emotional interest and not only + their linguistic characteristics. With regard to these + implications, this work is an important early piece as it laid + out a roadmap for some of the basic applications of sentiment + and emotion analysis of texts, namely sentiment and emotion + pattern recognition from text and computational text + characterization based on sentiment and emotion.</p> + <p>With the development of research methods used by digital + humanities researchers, the number of approaches and goals of + emotion and sentiment analysis in literature has grown. </p> + </div> + </div> + <div type="chapter"> + <p></p> + <p></p> + </div> + <div type="chapter"> + <head>2 Affect and Emotion</head> + <p>The history of emotion research has a long and rich tradition that followed + Darwin’s 1872 publication of <bibl> + <title type="desc">The Expression of the Emotions in Man and + Animals</title> + </bibl>.<note type="footnote"> + <ref type="bibliography" target="#darwin_expression_1872">Darwin 1872</ref>.</note> The subject of emotion theories + is vast and diverse. We refer the reader to Maria Gendron’s paper<note + type="footnote"> + <ref type="bibliography" target="#gendrin_past_2009">Gendron / Feldman Barrett 2009</ref>.</note> for a brief + history of ideas about emotion in psychology. Here, we will focus on three + views on emotion that are popular in computational analysis of emotions + (though they are, from a psychological perspective, motivated from different + perspectives and represent different elements of affect and emotion): + Ekman’s <term type="dh">theory of basic emotions</term>, Plutchik’s <term + type="dh">wheel of emotion</term>, and Russel’s <term type="dh" + >circumplex model</term>.</p> + <div type="subchapter"> + <head>2.1 Ekman’s Theory of Basic Emotions</head> + <p>The idea of basic emotion theories is that there are emotions that are + more "fundamental" than others. Mixtures of emotions which receive a + particular name are not necessarily defined as being basic. Attempts to + find a definition for emotions date back to Silvan Tomkins<note + type="footnote"> + <ref type="bibliography" target="#tomkins_consciousness_1962">Tomkins 1962</ref>.</note> in the early 1960s, who + characterized emotions based on similarities of stimuli and biological + processes, following the ideas that have been described already by + Charles Darwin – clearly an attempt that focuses on observations and + evolution. </p> + <p>One of Tomkins’ mentees, Paul Ekman, put in question the existing emotion + theories that proclaimed that facial expressions of emotion are socially + learned and therefore vary from culture to culture. Ekman, Sorenson and + Friesen challenged this view<note type="footnote"> + <ref type="bibliography" target="#ekman_elements_1969">Ekman et al. 1969</ref>, + pp. 86–88.</note> in a field study with the outcome that facial + displays of fundamental emotions are not learned but innate. However, + there are culture-specific prescriptions about how and in which + situations emotions are displayed. Based on the observation of facial + behavior in early development or social interaction, Ekman’s theory also + postulates that emotions should be considered <term type="dh">discrete + categories</term> + <note type="footnote"> + <ref type="bibliography" target="#ekman_expression_1993">Ekman 1993</ref>, p. 386.</note> rather than + continuous. Though this view allows for conceiving of emotions as having + different intensities, it does not allow emotions to blend and leaves no + room for more complex affective states in which individuals report the + <term type="dh">co-occurrence of like-valenced discrete + emotions</term>.<note type="footnote"> + <ref type="bibliography" target="#russel_recognition_1994">Russell + 1994</ref>; <ref type="bibliography" target="#russel_expressions_2003" + >Russell et al. 2003</ref>; <ref type="bibliography" + target="#gendron_emotion_2014">Gendron et al. 2014</ref>; <ref + type="bibliography" target="#feldman_emotions_2017">Feldman Barrett + 2017.</ref></note>.</p> + <p>Ekman and colleagues, however, defined clearly how basic emotions can be + distinguished from other emotions: There are distinctive universal + signals, the presence in other primates, distinctive phyiosology, + distinctive universals in antecedent events, coherence in the emotional + response, a quick onset, a brief duration, an automatic appraisal, and + an automatic, unbidden occurrence. The set + of emotions that is typically + referred to as "Ekman emotions" consists of anger, fear, joy, sadness, + surprise, and disgust. Given that this set of emotions is relevant for + many studies, and that these emotion categories do not deserve further + explanation to most people, it constitutes a popular basis for + computational analysis.</p> + </div> + <div type="subchapter"> + <head>2.2 Plutchik’s Wheel of Emotions</head> + <p>Another influential model of emotions was proposed by Robert Plutchik + in the early 1980s.<note type="footnote"> + <ref type="bibliography" target="#plutchik_emotions_1991">Plutchik 1991</ref>.</note> The + important difference between Plutchik’s theory and Ekman’s theory is + that apart from a small set of basic emotions, all other emotions + are mixed and derived from the various + combinations of basic ones. + He further categorized these other emotions into the <term type="dh" + >primary dyads</term> (very likely to co-occur), <term type="dh" + >secondary dyads</term> (less likely to co-occur) and <term + type="dh">tertiary dyads</term> (seldom co-occur). </p> + <p>In order to represent the organization and properties of emotions as + defined by his theory, Plutchik proposed a structural model of + emotions known nowadays as <bibl> + <title type="desc">Plutchik’s wheel of emotions</title> + </bibl>. The wheel (<ref type="graphic" + target="#emotion_analysis_2019_001">Figure 1</ref>) is constructed in the fashion of a color + wheel, with similar emotions placed closer together and opposite + emotions 180 degrees apart. The intensity of an emotion in the wheel + depends on how far from the center a part of a petal is, i.e., + emotions become less distinguishable the further they are from the + center of the wheel. Essentially, the wheel is constructed from + eight basic bipolar emotions: <hi rend="italic">joy</hi> versus <hi + rend="italic">sadness</hi>, <hi rend="italic">anger</hi> versus + <hi rend="italic">fear</hi>, <hi rend="italic">trust</hi> versus + <hi rend="italic">disgust</hi>, and <hi rend="italic" + >surprise</hi> versus <hi rend="italic">anticipation</hi>. The + blank spaces between the leaves are so-called <term type="dh" + >primary dyads</term> – emotions that are mixtures of two of the + primary emotions.</p> + <p>The <bibl> + <title type="desc">wheel model of emotions</title> + </bibl> proposed by Plutchik had a great impact on the field of + affective computing being primarily used as a basis for emotion + categorization in emotion recognition from text.<note type="footnote"> + <ref type="bibliography" target="#cambria_hourglass_2012">Cambria et al. + 2012</ref>; <ref type="bibliography" target="#kim_aspects_2012">Kim + et al. 2012</ref>; <ref type="bibliography" + target="#suttles_supervision_2013">Suttles / Ide 2013</ref>; <ref + type="bibliography" target="#borth_ontology_2013">Borth et al. + 2013</ref>; <ref type="bibliography" target="#abdul_emotion_2017" + >Abdul-Mageed / Ungar 2017</ref>.</note> + However, some postulates of the theory are criticized, for example, + there is no empirical support for the wheel structure.<note type="footnote"> + <ref type="bibliography" target="#smith_models_2009">Smith / Schneider + 2009</ref>. </note> Another + criticism is that Plutchik’s model of emotions does not explain the + mechanisms by which non-basic emotions emerge from the <hi + rend="italic">basic</hi> emotions, nor does it provide reliable + measurements of these emotions.<note type="footnote"> + <ref type="bibliography" target="#richins_emotions_1997">Richins 1997</ref>, + p. 128.</note> + </p> + <figure> + <graphic xml:id="emotion_analysis_2019_001" + url=".../medien/emotion_analysis_2019_001.png"> + <desc> + <ref target="#abb1">Fig. 1</ref>: Plutchik’s wheel of emotions. [<ref + type="bibliography" target="#plutchik_wheel_2011">Plutchik 2011</ref>. + <ref + target="https://creativecommons.org/publicdomain/mark/1.0/deed.de" + >PD</ref>] <ref type="graphic" target="#emotion_analysis_2019_001"/> + </desc> + </graphic> + </figure> + </div> + <div type="subchapter"> + <head>2.3 Russel’s Circumplex Model </head> + <p>Attempts to overcome the shortcomings of basic emotion theories + and its unfitness for clinical studies led researchers to + suggest various dimensional models, the most prominent of which + is the circumplex model of affect proposed by James Russel.<note + type="footnote"> + <ref type="bibliography" target="#russel_model_1980">Russell 1980</ref>.</note> The word <term + type="dh">circumplex</term> in the name of the model refers + to the fact that emotional episodes do not cluster at the axes + but rather at the periphery of a circle (<ref type="graphic" + target="#emotion_analysis_2019_002">Figure 2</ref>). At the core of + the circumplex model is the notion of two dimensions plotted on + a circle along horizontal and vertical axes. These dimensions + are <hi rend="italic">valence</hi> (how pleasant or unpleasant + one feels) and <hi rend="italic">arousal</hi> (the degree of + calmness or excitement). The number of dimensions is not + strictly fixed and there are adaptations of the model that + incorporate more dimensions. One example of this is the <term + type="dh">Valence-Arousal-Dominance model </term>that adds + an additional dimension of dominance, the degree of control one + feels over the situation that causes an emotion.<note + type="footnote"> <ref type="bibliography" target="#bradley_emotion_1994">Bradley / Lang + 1994</ref>, p. 50.</note> + </p> + <p>By moving from discrete categories to a dimensional + representation, the researchers are able to account for + subjective experiences that do not fit nicely into the isolated + non-overlapping categories. Accordingly, each affective + experience can be depicted as a point in a <hi rend="italic" + >circumplex</hi> that is described by only two parameters – + <hi rend="italic">valence</hi> and <hi rend="italic" + >arousal</hi> – without need for labeling or reference to + emotion concepts for which a name might only exist in particular + subcommunities or which are difficult to describe.<note + type="footnote"> <ref type="bibliography" target="#russel_affect_2003">Russell 2003</ref>, p. 154.</note> + However, the + strengths of the model turned out to be its weaknesses: for + example, it is not clear whether there are basic dimensions in + the model<note type="footnote"> <ref type="bibliography" target="#larsen_promises_1992">Larsen / Diener + 1992</ref>, p. + 25.</note> nor is it clear what should be done with + qualitatively different events of <hi rend="italic">fear</hi>, + <hi rend="italic">anger</hi>, <hi rend="italic" + >embarrassment</hi> and <hi rend="italic">disgust</hi> that + fall in identical places in the circumplex structure.<note + type="footnote"> <ref type="bibliography" target="#russel_affect_1999">Russell / Feldman + Barrett 1999</ref>, p. 807.</note> + Despite these shortcomings, the circumplex model of affect is + popular in psychologic and psycholinguistic studies, because + both dimensions can reliably be measured.<note type="footnote"> + <ref type="bibliography" target="#mauss_measures_2009">Mauss / Robinson 2009</ref>.</note> In computational linguistics, + the circumplex model is applied when the interest is in + continuous measurements of <hi rend="italic">valence</hi> and + <hi rend="italic">arousal</hi> rather than in the specific + discrete emotional categories.</p> + <p>There are other models which locate discrete emotion categories + in a dimensional space, however, these have not been used in + computational literary studies yet (though such approaches are + promising also in this domain and constitute promising future + research). One instance, next to valence/arousal, are appraisal + theories<note type="footnote"> + <ref type="bibliography" target="#scherer_emotions_2005">Scherer 2005</ref>.</note> which + state that different dimensions, which measure how a stimulus + event is cognitively evaluated enable different sets of + emotions. The work by Smith and Ellsworth<note type="footnote"> + <ref type="bibliography" target="#smith_patterns_1985">Smith / Ellsworth 1985</ref>.</note> shows that the six dimensions + of (1) how pleasant an event is, (2) how much effort an event + can be expected to cause, (3) how certain the experiencer is in + a specific situation, (4) how much attention is devoted to the + event, (5) how much responsibility the experiencer of the + emotion holds for what has happened, and (6) how much the + experiencer has control over the situation, explain 15 discrete + emotions.</p> + <figure> + <graphic xml:id="emotion_analysis_2019_002" + url=".../medien/emotion_analysis_2019_002.png"> + <desc> + <ref target="#abb2">Fig. 2</ref>: Circumplex model of affect: Horizontal + axis represents the valence dimension, the vertical axis represents the + arousal dimension. Drawn after <ref type="bibliography" + target="#posner_model_2005">Posner et al. 2005</ref>. [Kim / Klinger + 2019]<ref type="graphic" target="#emotion_analysis_2019_002"/> + </desc> + </graphic> + </figure> + + + </div> + </div> + <div type="chapter"> + <head>3 Emotion Analysis in Non-computational Literary Studies</head> + + <p>In the past, literary and art theories often disregarded the importance of + the aesthetic and affective dimension of literature, which in part stemmed + from the rejection of old-fashioned literary history that had explained the + meaning of art works by the biography of the author.<note type="footnote"> + <ref type="bibliography" target="#saetre_text_2014">Sætre et al. 2014b</ref>.</note> However, the affective turn taken by a wide + range of disciplines in the past two decades – from political and + sociological sciences to neurosciences or media studies – has refueled the + interest of literary critics in human affects and sentiments.</p> + <p>We said in <ref type="intern" target="#hd1">section 1</ref> that there seems + to be a consensus among literary critics that literary art and emotions go + hand in hand. However, one might be challenged to define the specific way in + which emotions come into play in the text. The exploration of this problem + is presented by van Meel.<note type="footnote"> <ref type="bibliography" target="#meel_emotions_1995">Van Meel 1995</ref>.</note> + Underpinning the centrality of human destiny, hopes, and feelings in the + themes of many artworks – from painting to literature – van Meel explores + how emotions are involved in the production of arts. Pointing out big + differences between the two media in their attempts to depict human emotions + (painting conveys nonverbal behavior directly, but lacks temporal dimensions + that novels have and use to describe emotions), van Meel provides an + analysis of the nonverbal descriptions used by the writers to convey their + characters’ emotional behavior. Description of visual characteristics, van + Meel speculates, responds to a fundamental need of a reader to build an + image of a person and their behavior. Moreover, nonverbal descriptions add + important information that can in some cases play a crucial hermeneutical + role, such as in Kafka’s <bibl> + <title type="desc">Der Prozess</title> + </bibl>, where the fatal decisions for K. are made clear by gestures rather + than words. His verdict is not announced, but is implied by the judge who + refuses a handshake. The same applies to his death sentence that is conveyed + to him by his executioners playing with a butcher’s knife above his head. + These aspects how emotions are communicated clearly point to challenges for + computational methods – implicit descriptions, world knowledge, and + inference steps that are grounded in combinations of text and readers' + experiences have not been tackled with computational methods yet.</p> + <p>A hermeneutic approach through the lense of emotions is presented by + Kuivalainen<note type="footnote"> <ref type="bibliography" target="#kuivalainen_emotions_2009">Kuivalainen + 2009</ref>.</note> and provides + a detailed analysis of linguistic features that contribute to the + characters’ emotional involvement in Katherine Mansfield’s prose. The study + shows how, through the extensive use of adjectives, adverbs, deictic + markers, and orthography, Mansfield steers the reader towards the + protagonist’s climax. Subtly shifting between psycho-narration and free + indirect discourse, Mansfield is making use of evaluative and emotive + descriptors in psycho-narrative sections, often marking the internal + discourse with dashes, exclamation marks, intensifiers, and repetition that + thus trigger an emotional climax. Various deictic features introduced in the + text are used to pinpoint the source of emotions, which helps in creating a + picture of characters’ emotional world. Verbs (especially in the present + tense), adjectives, and adverbs serve the same goal in Mansfield’s prose of + describing the characters’ emotional world. Going back and forth from + psycho-narration to free indirect discourse provides Mansfield with a tool + to point out the significant moments in the protagonists’ lives and + establish a separation between characters and narration. This study + illustrates another challenge for automatic methods. Computational models + mostly rely on isolated, comparable short, units of the text. The broader + context, let alone the development of characters, are mostly ignored in + computational analysis – a prediction depends on the local description and + is not conditioned on previous experiences. That is a clear disadvantage of + distant reading methods to close reading.</p> + <p>Both van Meel’s and Kuivalainen’s works, separated from each other by more + than a decade, underpin the importance of emotions in the interpretation of + characters’ traits, hopes, and tragedy. Other authors find these connections + as well. For example, Barton<note type="footnote"> <ref type="bibliography" target="#barton_character_1996">Barton 1996</ref>.</note> + proposes instructional approaches to teach school-level readers to interpret + character’s emotions and use this information for story interpretation. Van + Horn<note type="footnote"> <ref type="bibliography" target="#vanhorn_characters_1997">Van Horn 1997</ref>.</note> shows that + understanding characters emotionally or trying to help them with their + problems made reading and writing more meaningful for middle school + students. </p> + <p>Emotions in text are often conveyed with emotion-bearing words.<note + type="footnote"> <ref type="bibliography" target="#johnson_language_1989">Johnson-Laird / Oatley + 1989</ref>.</note> At the same time + their role in the creation and depiction of emotion should not be + overestimated. That is, saying that someone looked angry or fearful or sad, + as well as directly expressing characters’ emotions, are not the only ways + authors build believable fictional spaces filled with characters, action, + and emotions. In fact, many novelists strive to express emotions indirectly + by way of figures of speech or catachresis,<note type="footnote"> <ref type="bibliography" target="#miller_text_2014">Miller 2014</ref>, p. 92.</note> first of all because emotional language can be + ambiguous and vague, and, second, to avoid any allusions to Victorian + emotionalism and pathos.</p> + <p>How can an author convey emotions indirectly? A book chapter by Hillis Miller + in <bibl> + <title type="desc">Exploring Text and Emotions</title> + </bibl> + <note type="footnote"> <ref type="bibliography" target="#saetre_exploring_2014">Sætre et al. + 2014a</ref>, pp. 91ff.</note> seeks the answer + to exactly this question. Using Joseph Conrad’s <bibl> + <title type="desc">Nostromo</title> + </bibl> opening scenes as material, Miller shows how Conrad’s descriptions + of an imaginary space generate emotions in readers without direct + communication of emotions. Conrad’s <bibl> + <title type="desc">Nostromo</title> + </bibl> opening chapter is an objective description of Sulaco, an imaginary + land. The description is mainly topographical and includes occasional + architectural metaphors, but it combines wide expanse with hermetically + sealed enclosure, which generates <quote>depthless emotional + detachment</quote> + <note type="footnote"> <ref type="bibliography" target="#miller_text_2014">Miller 2014</ref>, p. 93.</note>. Through the use of + present tense, Conrad makes the readers suggest that the whole scene is + timeless and does not change. The topographical descriptions are given in a + pure materialist way: there is nothing behind clouds, mountains, rocks, and + sea that would matter to humankind, not a single feature of the landscape is + personified, and not a single topographical shape is symbolic. Knowingly or + unknowingly, Miller argues, by telling the readers what they should see – + with no deviations from truth – Conrad employs a trope that perfectly + matches Immanuel Kant’s <bibl> + <title type="desc">concept of the sublime</title> + </bibl>. Kant’s view of poetry was that true poets tell the truth without + interpretation; they do not deviate from what their eyes see. Conrad, or to + be more specific, his narrator in <bibl> + <title type="desc">Nostromo</title> + </bibl>, is an example of sublime seeing with a latent presence of strong + emotions. On the one hand, Conrad’s descriptions are cool and detached. This + coolness is caused by the indifference of the elements in the scene. On the + other hand, by dehumanizing sea and sky, Conrad generates <quote>awe, fear, + and a dark foreboding about the kinds of life stories that are likely to + be enacted against such a backdrop.</quote> + <note type="footnote"> <ref type="bibliography" target="#miller_text_2014">Miller 2014</ref>, p. 115.</note></p> + <p>Hillis Miller’s analysis resonates with some premises from emotion theory + that we have discussed previously, namely, Plutchik’s belief that emotions + should be studied not by a certain way of expression but by the overall + behavior of a person. Considering that such a formula cannot be applied to + all literary theory studies about emotions (as not all authors choose to + convey emotions indirectly, as well as not all authors tend to comment on + characters’ nonverbal emotional behavior), it seems that one should search + for a balance between low-level linguistic feature analysis of emotional + language and a rigorous high-level hermeneutic inquiry dissecting the form + of the novel and its under-covered philosophical layers.<note + type="footnote"> We recommend the essay by Katja Mellmann for further + details on that topic. <ref type="bibliography" target="#mellmann_emotion_2002">Mellmann 2002</ref>.</note> + </p> + </div> + <div type="chapter"> + <head>4 Emotion and Sentiment Analysis in Computational Literary Studies</head> + + <p>With this section, we proceed to an overview of the existing body of research + on computational analysis of emotion and sentiment in computational literary + studies. An overview of the papers including their properties is shown in + <ref + type="graphic" target="#emotion_analysis_2019_003">Table 1</ref>. The table, + as well as this section, is divided into several subsections, each of which + corresponds to a specific application of emotion analysis to literature. + <ref type="intern" target="#hd11">section 4.1</ref> reviews the papers + that deal with the classification of literary texts in terms of emotions + they convey; <ref type="intern" target="#hd14">section 4.2</ref> examines the + papers that address text classification by genre or other story-types based + on sentiment and emotion features; <ref type="intern" target="#hd17">section + 4.3</ref> is dedicated to research in modeling sentiments and emotions + in texts from previous centuries, as well as research dealing with + applications of sentiment analysis to texts written in the past; <ref + type="intern" target="#hd21">section 4.4</ref> provides an overview of + sentiment analysis applications to character analysis and character network + construction, and <ref type="intern" target="#hd24">section 4.5 </ref>is + dedicated to more general applications.</p> + <div type="subchapter"> + <head>4.1 Emotion Classification</head> + + <p>A straightforward approach to emotion analysis is text + classification<note type="footnote"> <ref type="bibliography" target="#liu_opinions_2015">Liu 2015</ref>, p. 47.</note>. + Indeed, emotion classification is one of the most popular subtasks and + finds application in several downstream tasks. A fundamental question of + such a classification is how to find the best input representations and + algorithms to classify the data (sentences, paragraphs, entire + documents) into predefined classes. When applied to literature, such a + classification may be of use for grouping different literary texts in + digital collections based on the emotional properties of the stories or + to perform other analyses regarding the distribution of emotions in + subcollections. For example, books or poems can be grouped based on the + emotions they convey or based on whether or not they have happy endings + or not.</p> + <div type="subchapter"> + <head>4.1.1 Classification based on emotions</head> + + <p>Barros et al.<note type="footnote"> <ref type="bibliography" target="#barros_classification_2013">Barros + et al. 2013</ref>.</note> aim at + answering two research questions: 1) is the classification of + Francisco de Quevedo’s works proposed by the literary scholars + consistent with the sentiment reflected by the corresponding poems; + and 2) which learning algorithms are the best for the classification + (the latter being an engineering question that is inherent in many + of the papers that we discuss)? They perform a set of experiments on + the classification of 185 Francisco de Quevedo’s poems that are + divided by literary scholars into four categories and that Barros et + al. map to emotions. Using the terms <hi rend="italic">joy</hi>, <hi + rend="italic">anger</hi>, <hi rend="italic">fear</hi>, and <hi + rend="italic">sadness</hi> as points of reference, Barros et al. + construct a list of emotion words by looking up the synonyms of + English emotion words and adjectives associated with these four + emotions and translating them into Spanish. This leads to a novel + and task-specific lexicon, to which each poem is then compared, + based on normalized term counts. The experiments show the + superiority of decision trees as classification approach which can + further be improved by rebalancing the collection via resampling. + Based on these results the authors conclude that a meaningful + classification of the literary pieces based only on the emotion + information is possible.</p> + <p>A more modern corpus selection of poetry is the object of analysis by + Ethan Reed.<note type="footnote"> <ref type="bibliography" target="#reed_poetry_2018">Reed 2018</ref></note>. The author + offers a <term type="dh">proof-of-concept</term> for performing + sentiment analysis on twentieth-century American poetry with + dictionary-based black-box sentiment analysis systems that output + the polarity of a text. Specifically, they analyze the expression of + emotions in the poetry of the <term type="dh">Black Arts + Movement</term> of the 1960s and 1970s. The goal of the project + is to understand how feelings associated with injustice are coded in + terms of race and gender, and what sentiment analysis can show us + about the relations between affect and gender in poetry. Reed notes + that the surface affective value of the words does not always align + with their more nuanced affective meaning shaped by poetic, social, + and political contexts. Therefore, this study can be seen as a + critical reflection on methodological choices.</p> + <p>Yu<note type="footnote"> <ref type="bibliography" target="#yu_evaluation_2008">Yu 2008</ref>.</note> explores linguistic patterns + that characterize the genre of sentimentalism in early American + novels. They analyze five novels from the mid-nineteenth century and + annotate the emotionality of each of the chapters as <hi + rend="italic">high</hi> or <hi rend="italic">low (not: positive + or negative!)</hi>. This approach is noteworthy, as the unit of + analysis is comparably large in contrast to most sentiment analysis + methods. Each chapter is classified with standard configurations of + support vector machines and naïve Bayes classifiers, as highly + emotional or the opposite. The results of the evaluation suggest + that arbitrary feature reduction steps such as stemming and stopword + removal should be taken very carefully, as they may affect the + prediction. </p> + <p>Volkova<note type="footnote"> <ref type="bibliography" target="#volkova_perception_2010">Volkova et al. 2010</ref>.</note> did not + focus on the classification of emotions automatically, but tackles + the task of annotation in more detail. The authors observe that + annotation of literature, in their case fairy tales, is challenging, + and that it is hard to obtain an acceptable annotation agreement. An + interesting innovative element in this study is that annotators were + not presented a predefined unit to annotate – they were allowed to + decide by themselves which granularity is most reasonable. That is + different to the other studies mentioned before in this section. + Further, a main finding was that short instances lead to a lower + agreement.</p> + <p>Finally, an interesting study by Ashok et al.<note type="footnote"> <ref type="bibliography" target="#ashok_success_2013">Ashok et al. 2013?</ref>.</note> did not classify + + emotions regarding a variable motivated by literary studies. They + use sentiment polarity as one component to predict the success of a + book. While such studies (similarly the prediction of citation + counts, etc.) are often criticized, the authors present some + interesting, but also perhaps non-surprising findings, e.g. that + unsuccessful stories contain more discriminative words that have a + negative connotation.</p> + </div> + <div type="subchapter"> + <head>4.1.2 Classification of happy ending vs. non-happy + endings</head> + + <p>A particular use case of emotion classification is to look closer + at particular parts of a text. Zehe et al.<note type="footnote"> + <ref type="bibliography" target="#zehe_prediction_2016">Zehe et al. + 2016</ref>.</note> argue that automatically + recognizing a happy ending as a major plot element could help to + better understand a plot structure as a whole. To show that this + is possible, they classify 212 German novels written between + 1750 and 1920 as having happy or non-happy endings. A novel is + considered to have a happy ending if the situation of the main + characters in the novel improves towards the end or is + constantly favorable. The novels were manually annotated with + this information by domain experts. For feature extraction, the + authors first split each novel into <hi rend="italic">n</hi> + segments of the same length. They then calculate sentiment + values for each of the segments based on a normalized word + frequency with a German version of the <bibl> + <title type="desc">NRC Word-Emotion Association + Lexicon</title>. + </bibl> + <note type="footnote"> <ref type="bibliography" target="#mohammad_crowdsourcing_2013">Mohammad / + Turney 2013</ref>.</note> An + automatic sentiment classification with support vector machines + achieves reasonable and encouraging results.</p> + </div> + </div> + <div type="subchapter"> + <head>4.2 Genre and Story-type Classification</head> + + <p>The papers we have discussed so far focus on understanding + the emotion associated with units of texts. This extracted + information can further be used for downstream tasks and + also for downstream evaluations. In the following, we + discuss downstream classification cases. The papers in this + category use sentiment and emotion features for a + higher-level classification, namely story-type clustering + and literary genre classification. The assumption behind + these works is that different types of literary text may + show different composition and distribution of emotion + vocabulary and thus can be classified based on this + information. The hypothesis that different literary genres + convey different emotions stems from common knowledge: we + know that horror stories instill <hi rend="italic">fear</hi> + and that mysteries evoke <hi rend="italic">anticipation</hi> + and <hi rend="italic">anger</hi> while romances are filled + with <hi rend="italic">joy</hi> and <hi rend="italic" + >love</hi>. However as we will see in this section, the + task of automatic classification of these genres is not + always that straightforward and reliable. </p> + <div type="subchapter"> + <head>4.2.1 Story-type clustering</head> + + <p>Similarly to Zehe et al., Reagan et al.<note type="footnote"> + <ref type="bibliography" target="#reagan_arcs_2016">Reagan et al. + 2016</ref>.</note> are + interested in automatically understanding a plot + structure as a whole, but not limited to a book ending. + The inspiration for their work comes from Kurt + Vonnegut’s lecture on emotional arcs of stories.<note type="footnote"> <ref type="bibliography" target="#vonnegut_blackboard_2010">Vonnegut 2010 + (2005)</ref>.</note> Reagan + et al. test the idea that the plot of each story can be + visualized as an <hi rend="italic">emotional arc</hi>, + i.e., a time series graph, where the <hi rend="italic" + >x</hi>-axis represents a time point in a story, and + the <hi rend="italic">y</hi>-axis represents the events + happening to the main characters that can be favorable + (peaks on a graph) or unfavorable (troughs on a graph). + As Vonnegut puts it, the stories can be grouped by these + <hi rend="italic">arcs</hi> and the number of such + groupings is limited. To test this idea, Reagan et al. + collect the 1,327 most popular books from the <ref + target="https://www.gutenberg.org/">Project + Gutenberg</ref>.<note type="footnote"> + <ref type="bibliography" target="#project_gutenberg_2019">Project + Gutenberg 1971–2019</ref> [<hi rend="italic" + >Webseite aus Deutschland nicht mehr erreichbar</hi>].</note> Each book is then split into + segments for which happiness scores are calculated and + compared. The results of the analysis show support for + six emotional patterns that are shared between + subgroupings of the corpus. Additionally, Reagan et al. + find that some patterns are more popular among readers, + based on download counts, than others.</p> + </div> + <div type="subchapter"> + <head>4.2.2 Genre classification</head> + + <p>There are other studies<note type="footnote"> + <ref type="bibliography" target="#samothrakis_annotation_2015" + >Samothrakis / Fasli 2015</ref>; <ref type="bibliography" + target="#kim_relationship_2017">Kim et al. 2017a</ref>; <ref + type="bibliography" target="#kim_emotion_2017">Kim et al. + 2017b</ref>.</note> that are similar in spirit to + the work done by Reagan et al. Samothrakis and Fasli + examine the hypothesis that different genres clearly + have different emotion patterns to reliably classify + them with machine learning. To that end, they + collect works of the genres <hi rend="italic" + >mystery</hi>, <hi rend="italic">humor</hi>, <hi + rend="italic">fantasy</hi>, <hi rend="italic" + >horror</hi>, <hi rend="italic">science + fiction</hi> and <hi rend="italic">western</hi> + from the Project + Gutenberg. Using <ref + target="https://wndomains.fbk.eu/wnaffect.html" + >WordNet-Affect</ref> + <note type="footnote"> <ref type="bibliography" target="#strapparava_extension_2004">Strapparava + / Valitutti 2004</ref>.</note> to detect emotion words as + categorized by Ekman’s fundamental emotion classes, + they calculate an emotion score for each sentence in + the text. Each work is then transformed into six + vectors, one for each basic emotion. With a random + forrest classifier, they show that genre + classification is possible based on this information + with performance scores significantly above + average.</p> + <p>The study by Kim et al.<note type="footnote"> <ref type="bibliography" target="#kim_relationship_2017">Kim et al. + 2017a</ref>.</note> originates from the same + premise as the work by Samothrakis and Fasli but + puts emphasis on finding genre-specific correlations + of emotion developments. They therefore link the + motivation of Reagan et al. with the one by + Samothrakis and Fasli. Extending the set of tracked + emotions to Plutchik’s classification, Kim et al. + collect 2,000 books from the Project Gutenberg that + belong to five genres found in the Brown corpus,<note + type="footnote"> <ref type="bibliography" target="#francis_corpus_1979">Francis / Kucera + 1979</ref>.</note> + namely <hi rend="italic">adventure</hi>, <hi + rend="italic">science fiction</hi>, <hi + rend="italic">mystery</hi>, <hi rend="italic" + >humor</hi> and <hi rend="italic">romance</hi>. + The authors extend the set of classification + algorithms beyond random forests using a <term + type="dh">multi-layer perceptron</term> and <term + type="dh">convolutional neural networks</term>, + which achieves the best performance. To understand + how uniform the emotion patterns in different genres + are, the authors introduce the notion of <hi + rend="italic">prototypicality</hi>, which is + computed as average of all emotion scores. Using + this as a point of reference for each genre Kim et + al. use Spearman correlation to calculate the + uniformity of emotions per genre. The results of + this analysis suggest that <hi rend="italic" + >fear</hi> and <hi rend="italic">anger</hi> are + the most salient plot devices in fiction, while <hi + rend="italic">joy</hi> is only of mediocre + stability, which is in line with findings of + Samothrakis and Fasli.</p> + <p>The study by Henny-Khramer<note type="footnote"> + <ref type="bibliography" target="#henny_exploration_2018">Henny-Krahmer + 2018</ref>.</note> pursues two goals: 1), + to test whether different subgenres of Spanish + American literature differ in degree and kind of + emotionality, and 2), whether emotions in the novels + are expressed in direct speech of characters or in + narrated text. To that end, they conduct a subgenre + classification experiment on a corpus of Spanish + American novels using sentiment values as features. + To answer the first question, each novel is split + into five segments and for each sentence in the + segment the emotion score (polarity values + + Plutchik’s basic emotions) is calculated using <ref + target="https://github.com/aesuli/SentiWordNet" + >SentiWordNet</ref> + <note type="footnote"> <ref type="bibliography" target="#baccianella_resource_2010">Baccianella + et al. 2010</ref>.</note> and <ref + target="https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm" + >NRC</ref><note type="footnote"> + <ref type="bibliography" target="#mohammad_crowdsourcing_2013">Mohammad / + Turney 2013</ref>.</note> dictionaries. The analysis of feature + importance shows that the most salient features come + from the sentiment scores calculated from the + characters’ direct speech and that novels with + higher values of positive speech are more likely to + be sentimental novels. This is an interesting + variant of the beforehand mentioned studies – it is + important to distinguish characters' speech from + other parts of the text.</p> + <p>There are some limitations to the studies presented + in this section. On the one hand, it is questionable + how reliable <term type="dh">coarse emotion + scoring</term> is that takes into account only + presence or absence of words found in specialized + dictionaries and overlooks negations and modifiers + that can either negate an emotion word or + increase/decrease its intensity. On the other hand, + a limited view of the emotional content as a sum of + emotion bearing words reserves no room for + qualitative interpretation of the texts – it is not + clear how one can distinguish between emotion words + used by the author to express their sentiment, + between words used to describe characters’ feelings, + and emotion words that characters use to address or + describe other characters in a story.</p> + </div> + </div> + <div type="subchapter"> + <head>4.3 Structural Changes of Sentiment</head> + + <p>The papers that we have reviewed so far approach + the problem of sentiment and emotion analysis as a + classification task. However, applications of + sentiment analysis are not only limited to + classification. In other fields, for example + computational social sciences, sentiment analysis + can be used for analyzing political preferences of + the electorate or for mining opinions about + different products or topics. Similarly, several + digital humanities studies incorporate sentiment + analysis methods in a task of mining sentiments + and emotions of people who lived in the past. The + goal of these studies is not only to recognize + sentiments, but also to understand how they were + formed.</p> + <div type="subchapter"> + <head>4.3.1 Topography of emotions</head> + + <p>Heuser et al.<note type="footnote"> <ref type="bibliography" target="#heuser_emotions_216">Heuser et al. + 2016</ref>.</note> start with a premise that + emotions occur at a specific moment in time and + space, thus making it possible to link emotions to + specific geographical locations. Consequently, + having such information at hand, one can + understand which emotions are hidden behind + certain landmarks. As a <term type="dh" + >proof-of-concept</term>, Heuser et al. build an + <ref + target="https://www.historypin.org/en/victorian-london/"> interactive map of emotions</ref> + in Victorian London<note type="footnote"> + <ref type="bibliography" target="#historypin_map_2017">Historypin + 2010–2017</ref>.</note> + where each location is tagged with emotion labels. + The underlying corpus for their analysis consists + of English books from the eighteenth and + nineteenth century, from which they extract + frequently mentioned geographical locations of + London. The presegmented data is then given to + annotators who are asked to define whether each of + the passages expressed <hi rend="italic" + >happiness</hi> or <hi rend="italic">fear</hi>, or + <hi rend="italic">neutrality</hi>. The same data + is further analyzed with a dictionary-based + sentiment classifier.</p> + <p>Some striking observations are made with regard + to the data analysis. First, there is a clear + discrepancy between fiction and reality – while + toponyms from the West End with Westminster and + the City are over-represented in the books, the + same does not hold true for the East End with + Tower Hamlets, Southwark, and Hackney. Hence, + there is less information about emotions + pertaining to these particular London locations. + Another striking detail is that the resulting map + is dominated by the neutral emotion. Heuser et al. + argue that this has nothing to do with the absence + of emotions but rather stems from the fact that + emotions tend to be silenced in public domain, + which influenced the annotators decision. </p> + <p>The space and time context are also used by + Bruggman and Fabrikant<note type="footnote"> + <ref type="bibliography" target="#bruggmann_text_2014">Bruggmann / + Fabrikant 2014</ref>.</note> who model + sentiments of Swiss historians towards places in + Switzerland in different historical periods. As + the authors note, it is unlikely that a historian + will directly express attitudes towards certain + toponyms, but it is very likely that words they + use to describe those can bear some negative + connotation (e.g. cholera, death). + Correspondingly, such places should be identified + as bearing negative sentiment by a sentiment + analysis tool. Additionally, they study the + changes of sentiment towards a particular place + over time. Using the <bibl> + <title type="desc">General Inquirer</title> + </bibl> (GI) lexicon<note type="footnote"> <ref type="bibliography" target="#stone_inquirer_19688">Stone et al. + 1968</ref>.</note> to identify positive and + negative terms in the document, they assign + sentiment scores and conclude that the results of + their analysis look promising, especially + regarding negatively scored articles.</p> + </div> + <div type="subchapter"> + <head>4.3.2 Tracking sentiment</head> + + <p>Other papers in this category link sentiment + and emotion to certain groups, rather than + geographical locations. The goal of these studies + is to understand how sentiment within and towards + these groups was formed. </p> + <p>Taboada et al.<note type="footnote"> <ref type="bibliography" target="#taboada_classification_2006">Taboada + et al. 2006</ref>; <ref type="bibliography" + target="#taboada_reputation_2008">Taboada et al. 2008</ref>.</note> aim at + tracking the literary reputation of six authors + writing in the first half of the twentieth + century. The research questions raised in the + project are how the reputation is made or lost, + and how to find correlation between what is + written about the authors and their work to the + authors’ reputation and subsequent canonicity. The + project’s goal is to examine critical reviews of + six authors’ writing and to map information + contained in texts critical to the author’s + reputation. The material they work with includes + not only reviews, but also press notes, press + articles, and letters to editors (including from + the authors themselves). They collected and + scanned 330 documents and tagged them with + polarity words with custom-made sentiment + dictionaries. The sentiment orientation of + rhetorically important parts of the texts is then + measured. The authors conclude that the current + approach has mostly been limited by a + non-sufficiently large lexicon.</p> + <p>Chen et al.<note type="footnote"> + <ref type="bibliography" target="#chen_people_2012">Chen et al. + 2012</ref>.</note> aim to understand personal narratives + of Korean <hi rend="italic">comfort women</hi> who + had been forced into sexual slavery by Japanese + military during World War II. Adapting the <bibl> + <title type="desc">WordNet-Affect</title> + </bibl> lexicon,<note type="footnote"> <ref type="bibliography" target="#strapparava_extension_2004"> + Strapparava / Valitutti 2004</ref>.</note> Chen et al. build their + own emotion dictionary to spot keywords in women’s + stories and map the sentences to emotion + categories. By adding variables of time and space, + Chen et al. provide a unified framework of + collective remembering of this historical event as + witnessed by the victims.</p> + <p>An interesting methodological contribution has + been made by Gao et al.<note type="footnote"> + <ref type="bibliography" target="#gao_multiscale_2016">Geo et al. 2016</ref>.</note> Instead of using raw counts of + polarity words over time, they propose that + filters are used to smooth the time series, which + further allows for other downstream + applications.</p> + </div> + <div type="subchapter"> + <head>4.3.3 Sentiment recognition in historical + texts</head> + + <p>Other papers put emphasis not so much on the + sentiments expressed by writers but instead focus + on the particularities of historical language. + Marchetti et al.<note type="footnote"> <ref type="bibliography" target="#marchetti_analysis_2014">Marchetti + et al. 2014</ref>.</note> and Sprugnoli et al.<note + type="footnote"> <ref type="bibliography" target="#sprugnoli_analysis_2016">Sprugnoli + et al. 2016</ref>.</note> + present the integration of sentiment analysis in + the <ref target="https://alcidedigitale.fbk.eu/">ALCIDE</ref> (Analysis of Language and Content in + a Digital Environment) project.<note type="footnote"> + <ref type="bibliography" target="#alcide_cit_2014">ALCIDE Demo + 2014–2015</ref>.</note> The sentiment + analysis module is based on <term type="dh" + >WordNet-Affect</term>, <term type="dh" + >SentiWordNet</term> + <note type="footnote"> <ref type="bibliography" target="#baccianella_resource_2010">Baccianella + et al. 2010</ref>.</note> and <term type="dh" + >MultiWordNet</term>.<note type="footnote"> <ref type="bibliography" target="#pianta_database_2002">Pianta et al. + 2002</ref>.</note> Each document is assigned a + normalized polarity score. The overall conclusion + of their work is that the assignment of a polarity + in the historical domain is a challenging task + largely due to lack of agreement on polarity of + historical sources between human annotators.</p> + <p>Challenged by the problem of applicability of + existing emotion lexicons to historical texts, + Buechel et al.<note type="footnote"> <ref type="bibliography" target="#buechel_course_2017">Buechel et al. + 2017</ref>.</note> propose a new method of + constructing affective lexicons that would adapt + well to German texts written up to three centuries + ago. In their study, Buechel et al. use the + representation of affect based on the <term + type="dh">Valence-Arousal-Dominance model</term> + (an adaptation of Russel’s circumplex model, see + + <ref type="intern" target="#hd8">section + + 2.3</ref>). Presumably, such a representation + provides a finer-grained insight into the literary + text,<note type="footnote"> <ref type="bibliography" target="#buechel_feelings_2016">Buechel et al. + 2016</ref> p. + 54, p. 59.</note>, which is more expressive than + discrete categories, as it quantifies the emotion + along three different dimensions. As a basis for + the analysis, they collect German texts from the + <ref target="http://www.deutschestextarchiv.de/" + >Deutsches Textarchiv</ref> <note type="footnote"> + <ref type="bibliography" target="#bbaw_dta_2019">Deutsches Textarchiv + 2007–2019</ref>.</note> written between 1690 + and 1899. The corpus is split into seven slices, + each spanning 30 years. For each slice they + compute word similarities and obtain seven + distinct emotion lexicons, each corresponding to + specific time period. This allows for, the authors + argue, the tracing of the shift in emotion + association of words over time. </p> + <p>Finally, Leemans et al.<note type="footnote"> + <ref type="bibliography" target="#leemans_emotions_2017">Leemans et al. + 2017</ref>.</note> aim to trace + historical changes in emotion expressions and to + develop methods to trace these changes in a corpus + of 29 Dutch language theatre plays written between + 1600 and 1800. Expanding the Dutch version of <bibl> + <title type="desc">Linguistic Inquiry and Word + Count</title> + </bibl> (LIWC) dictionary<note type="footnote"> + <ref type="bibliography" target="#pennebaker_development_2007">Pennebaker + et al. 2007</ref>.</note> with historical + terms, the authors are able to increase the recall + of emotion recognition with a dictionary. In + addition, they develop a fine-grained vocabulary + mapping body terms to emotions, and show that a + combination of LIWC and their lexicon lead to + improvement in the emotion recognition. </p> + </div> + </div> + <div type="subchapter"> + <head>4.4 Character Network Analysis and + Relationship Extraction</head> + + <p>The papers reviewed above address sentiment + analysis of literary texts mainly on a document + level. This abstraction is warranted if the goal + is to get an insight into the distribution of + emotions in a corpus of books. However, emotions + depicted in books do not exist in isolation but + are associated with characters who are at the core + of any literary narrative.<note type="footnote"> + <ref type="bibliography" target="#ingermanson_fiction_2009">Ingermanson / + Economy 2009</ref>, p. 107.</note> This + leads us to ask what sentiment and emotion + analysis can tell us about the characters. How + emotional are they? And what role do emotions play + in their interaction?</p> + <p>Character relationships have been analyzed in + computational linguistics from a graph theoretic + perspective, particularly using social network + analysis.<note type="footnote"> <ref type="bibliography" target="#agarwal_extraction_2013">Agarwal et al. + 2013</ref>; <ref type="bibliography" target="#elson_networks_2011">Elson + et al. 2011</ref>.</note> Fewer works, + however, address the problem of modeling character + relationships in terms of sentiment. Below we + provide an overview of several papers that propose + the methodology for extracting this information. </p> + <div type="subchapter"> + <head>4.4.1 Sentiment dynamics between + characters</head> + + <p>Several studies present automatic methods for + analyzing sentiment dynamics between plays’ + characters. The goal of the study by Nalisnick and + Baird<note type="footnote"> <ref type="bibliography" target="#nalisnick_analysis_2013">Nalisnick / + Baird 2013a</ref>.</note> is to track the emotional + trajectories of interpersonal relationships. The + structured format of a dialog allows them to + identify who is speaking to whom, which makes it + possible to mine character-to-character sentiment + by summing the valence values of words that appear + in the continuous direct speech and are found in + the lexicon<note type="footnote"> <ref type="bibliography" target="#nielsen_lexicon_2011">Nielsen + 2011</ref>.</note> of affective norms. The + extension<note type="footnote"> <ref type="bibliography" target="#nalisnick_networs_2013">Nalisnick / + Baird 2013b</ref>.</note> of the previous research from the + same authors introduces the concept of a + <quote>sentiment network</quote>, a dynamic social + network of characters. Changing polarities between + characters are modeled as edge weights in the + network. Motivated by the desire to explain such + networks in terms of a general sociological model, + Nalisnick and Baird test whether Shakespeare’s + plays obey the <term type="dh">Structural Balance + Theory</term> by Marvel et al.<note + type="footnote"> <ref type="bibliography" target="#marvel_model_2011">Marvel et al. + 2011</ref>.</note> that + postulates that a friend of a friend is also your + friend. Using the procedure proposed by Marvel et + al. on their Shakespearean sentiment networks, + Nalisnick and Baird test whether they can predict + how a play’s characters will split into factions + using only information about the state of the + sentiment network after Act II. The results of + their analysis are varied and do not provide + adequate support for the Structural Balance Theory + as a benchmark for network analysis in + Shakespeare’s plays. One reason for that, as the + authors state, is inadequacy of their shallow + sentiment analysis methods that cannot detect such + elements of speech as irony and deceit that play a + pivotal role in many literary works. </p> + </div> + <div type="subchapter"> + <p></p> + <p></p> + <p></p> + </div> + <div type="subchapter"> + <head>4.4.2 Character analysis and character + relationships</head> + + <p>Elsner<note type="footnote"> <ref type="bibliography" target="#elsner_kernels_2012">Elsner 2012</ref>; + <ref type="bibliography" target="#elsner_representations_2015" + >Elsner 2015</ref>.</note> aims at answering the question + of how to represent a plot structure for + summarization and generation tools. To that end, + Elsner presents a <hi rend="italic">kernel</hi> + for comparing novelistic plots at the level of + character interactions and their relationships. + Using sentiment as one of the properties of a + character, Elsner demonstrates that the kernel + approach leads to meaningful plot representation + that can be used for a higher-level + processing.</p> + <p>Kim and Klinger<note type="footnote"> <ref type="bibliography" target="#kim_annotation_2018">Kim / Klinger + 2018</ref>.</note> aim at understanding the + causes of emotions experienced by literary + characters. To that end, they contribute the <ref + target="http://www.ims.uni-stuttgart.de/data/reman" + >REMAN corpus</ref> <note type="footnote"> + <ref type="bibliography" target="#reman_corpus_2019">REMAN – Relational + Emotion Annotation for Fiction. Corpus 2018</ref>.</note> of literary texts with + annotations of emotions, experiencers, causes and + targets of the emotions. The goal of the project + is to enable the automatic extraction of emotions + and causes of emotions experienced by the + characters. The authors suggest that the results + of coarse-grained emotion classification in + literary text are not readily interpretable as + they do not tell much about who the experiencer of + the emotion is. Indeed, if a text mentions two + characters, one of whom is <hi rend="italic" + >angry</hi> and another one who is <hi + rend="italic">scared</hi> because of that, text + classification models will only tell us that the + text is about <hi rend="italic">anger</hi> and <hi + rend="italic">fear</hi>. Hence, a finer-grained + approach towards character relationship extraction + is warranted. Kim and Klinger conduct experiments + on the annotated dataset showing that the + fine-grained approach to emotion prediction with + long short-term memory networks outperforms <term + type="dh">bag-of-words models</term>. At the same + time, the results of their experiments suggest + that joint prediction of emotions and experiencers + can be more beneficial than studying these + categories separately.</p> + <p>A tool presented by Jhavar and Mirza<note + type="footnote"> <ref type="bibliography" target="#jhavar_emotions_2018">Jhavar / Mirza + 2018</ref>.</note> + provides a similar functionality: given an input + of two character names from the <bibl> + <title type="desc">Harry Potter</title> + </bibl> series, the <ref + target="https://gate.d5.mpi-inf.mpg.de/emofiel/" + >EMoFiel</ref> + <note type="footnote"> + <ref type="bibliography" target="#emofiel_mpg_2018">EMoFiel: Emotion + Mapping of Fictional Relationship 2018</ref>.</note> tool + identifies the emotion flow between a given + directed pair of story characters. These emotions + are identified using categorical<note + type="footnote"> <ref type="bibliography" target="#plutchik_emotions_1991">Plutchik + 1991</ref>.</note> and + continuous<note type="footnote"> <ref type="bibliography" target="#russel_model_1980">Russell 1980</ref>.</note> emotion models.</p> + <p>Egloff et al.<note type="footnote"> <ref type="bibliography" target="#egloff_model_2018">Egloff et al. + 2018</ref>.</note> present an ongoing work on the + <term type="dh">Ontology of Literary + Characters</term> (OLC) that allows us to capture + and infer characters’ psychological traits from + their linguistic descriptions. The OLC + incorporates the <term type="dh">Ontology of + Emotion</term> + <note type="footnote"> <ref type="bibliography" target="#patti_explration_2015">Patti et al. + 2015</ref>.</note> + that is based on both Plutchik’s and + Hourglass’s<note type="footnote"> <ref type="bibliography" target="#cambria_hourglass_2012">Cambria et al. + 2012</ref>.</note> models of emotions. The ontology + encodes 32 emotion concepts. Based on their + natural language description, characters are + attributed to a psychological profile along the + classes of <hi rend="italic">Openness</hi> to <hi + rend="italic">experience</hi>, <hi rend="italic" + >Conscientiousness</hi>, <hi rend="italic" + >Extraversion</hi>, <hi rend="italic" + >Agreeableness</hi>, and <hi rend="italic" + >Neuroticism</hi>. The ontology links each of + these profiles to one or more archetypal + categories of <hi rend="italic">hero</hi>, <hi + rend="italic">anti-hero</hi>, and <hi + rend="italic">villain</hi>. Egloff et al. argue + that, by using the semantic connections of the + OLC, it is possible to infer the characters’ + psychological profiles and the role they play in + the plot. </p> + <p>Kim and Klinger<note type="footnote"> <ref type="bibliography" target="#kim_friendship_2019">Kim / Klinger + 2019b</ref>.</note> propose the task of emotion + relationship classification between fictional + characters. They argue that joining character + network analysis with sentiment and emotion + analysis may contribute to a computational + understanding of narrative structures, as + characters are at the center of any plot + development. Building a corpus of 19 fan fiction + short stories and annotating it with emotions, Kim + and Klinger propose several models to classify + emotion relations of characters. They show that a + deep learning architecture with character position + indicators is the best for the task of predicting + both directed and undirected emotion relations in + the associated social network graph. As an + extension to this study, Kim and Klinger<note + type="footnote"> <ref type="bibliography" target="#kim_analysis_2019">Kim / Klinger + 2019a</ref>.</note> + explore how emotions are expressed between + characters in the same corpus via various + non-verbal communication channels.<note + type="footnote"> Their analysis is based on <ref + type="bibliography" target="#meel_emotions_1995">Van Meel 1995</ref> we mentioned in <ref type="intern" + + target="#hd9">section 3</ref>.</note> They find + + that facial expressions are predominantly + associated with <hi rend="italic">joy</hi> while + gestures and body postures are more likely to + occur with <hi rend="italic">trust</hi>.</p> + <p>Finally, a small body of work focuses on + mathematical modeling of character relationships. + Rinaldi et al.<note type="footnote"> <ref type="bibliography" target="#rinaldi_discoveries_2013">Rinaldi + et al. 2013</ref>.</note> contribute a model that + describes the love story between the <bibl> + <title type="desc">Beauty and the Beast</title> + </bibl> through ordinary differential equations. + Zhuravlev et al.<note type="footnote"> <ref type="bibliography" target="#zhuravlev_affairs_2014">Zhuravlev + et al. 2014</ref>.</note> introduce a distance function + to model the relationship between the protagonist + and other characters in two masochistic short + novels by Ivan Turgenev and Sacher-Masoch. + Borrowing some instruments from the literary + criticism and using ordinary differential + equations, Zhuravlev et al. are able to reproduce + the temporal and spatial dynamics of the love plot + in the two novellas more precisely than it had + been done in previous research. Jafari et al.<note + type="footnote"> <ref type="bibliography" target="#jafri_story_2016">Jafari et al. + 2016</ref>.</note> + present a dynamic model describing the development + of character relationships based on differential + equations. The proposed model is enriched with + complex variables that can represent complex + emotions such as coexisting <hi rend="italic" + >love</hi> and <hi rend="italic">hate</hi>.</p> + </div> + </div> + <div type="subchapter"> + <head>4.5 Other Types of Emotion Analysis</head> + + <p>We have seen that sentiment analysis as applied + to literature can be used for a number of + downstream tasks, such as classification of texts + based on the emotions they convey, genre + classification based on emotions, and sentiment + analysis in the historical domain. However, the + application of sentiment analysis is not limited + to these tasks. In this concluding part of the + survey, we review some papers that do not + formulate their approach to sentiment analysis as + a downstream task. Often, the goal of these works + is to understand how sentiments and emotions are + represented in literary texts in general, and how + sentiment or emotion content varies across + specific documents or a collection of them with + time, where time can be either relative to the + text in question (from beginning to end) or to the + historical changes in language (from past to + present). Such information is valuable for gaining + a deeper insight into how sentiments and emotions + change over time, allowing us to bring forward new + theories or shed more light onto existing literary + or sociological theories.</p> + <div type="subchapter"> + <head>4.5.1 Emotion flow analysis and + visualization</head> + + <p>A set of authors aimed to visualize the change + of emotion content through texts or across time. + One of the earliest works in this direction is a + paper by Anderson and McMaster<note + type="footnote"> <ref type="bibliography" target="#anderson_tone_1986"> Anderson / + McMaster 1986</ref>.</note> + that starts from the premise that reading + enjoyment stems from the affective tones of a + text. These affective tones create a conflict that + can rise to a climax through a series of crises, + which is necessary for a work of fiction to be + attractive to the reader. Using a list of 1,000 of + the most common English words annotated with + valence, arousal, and dominance ratings,<note + type="footnote"> <ref type="bibliography" target="#heise_profiles_1965">Heise 1965</ref>.</note> they calculate + the conflict score by taking the mean of the + ratings for each word in a text passage. The more + negative the score is, the higher the conflict is, + and vice versa. Additionally, they plot conflict + scores for each consecutive 100 words of a test + story and provide qualitative analysis of the + peaks. They argue that a reader who has access to + the text would be able to find correlation between + events in the story and peaks on the graph. + However, the authors still stress that such + interpretation remains dependent upon the + judgement of the reader. Further, other + contributions by the authors are based on the same + premises.<note type="footnote"> <ref type="bibliography" target="#anderson_computer_1982">Anderson / + McMaster 1982</ref>; <ref type="bibliography" + target="#anderson_tone_1993">Anderson / McMaster 1993</ref>.</note> + </p> + <p>Alm and Sproat<note type="footnote"> <ref type="bibliography" target="#alm_sequencing_2005">Alm / Sproat + 2005</ref>.</note> present the results of the + emotion annotation task of 22 tales by the Grimm + brothers and evaluate patterns of emotional story + development. They split emotions into <hi + rend="italic">positive</hi> and <hi rend="italic" + >negative</hi> categories and divide each story + into five parts from which aggregate frequency + counts of combined emotion categories are + computed. The resulting numbers are plotted on a + graph that shows a wave-shaped pattern. From this + graph, Alm and Sproat argue, one can see that the + first part of the fairy tales is the least + emotional, which is probably due to scene setting, + while the last part shows an increase in positive + emotions, which may signify the happy ending.</p> + <p>Two other studies by Mohammad<note + type="footnote"> <ref type="bibliography" target="#mohammad_time_2011">Mohammad + 2011</ref>; <ref type="bibliography" target="#mohammad_time_2012" + >Mohammad 2012</ref>.</note> focus on differences in emotion word + density as well as emotional trajectories between + books of different genres. Emotion word density is + defined as a number of times a reader will + encounter an emotion word on reading every <hi + rend="italic">X words</hi>. In addition, each text + is assigned several emotion scores for each + emotion that are calculated as a ratio of words + associated with one emotion to the total number of + emotion words occurring in a text. Both metrics + use the <term type="dh">NRC Affective + Lexicon</term> to find occurrences of emotion + words. They find that fairy tales have + significantly higher <hi rend="italic" + >anticipation</hi>, <hi rend="italic" + >disgust</hi>, <hi rend="italic">joy</hi> and <hi + rend="italic">surprise</hi> word densities, but + lower <hi rend="italic">trust</hi> word densities + when compared to novels. </p> + <p>A work by Klinger et al.<note type="footnote"> + <ref type="bibliography" target="#klinger_emotion_2016">Klinger et al. + 2016</ref>.</note> is a case study in an + automatic emotion analysis of Kafka’s <bibl> + <title type="desc">Amerika</title> + </bibl> and <bibl> + <title type="desc">Das Schloss</title> + </bibl>. The goal of the work is to analyze the + development of emotions in both texts as well as + to provide a character-oriented emotion analysis + that would reveal specific character traits in + both texts. To that end, Klinger et al. develop + German dictionaries of words associated with + Ekman’s fundamental emotions plus contempt and + apply them to both texts in question to + automatically detect emotion words. The results of + their analysis for <bibl> + <title type="desc">Das Schloss</title> + </bibl> show a striking increase of <hi + rend="italic">surprise</hi> towards the end and a + peak of <hi rend="italic">fear</hi> shortly after + start of chapter 3. In the case of <bibl> + <title type="desc">Amerika</title> + </bibl>, the analysis shows that there is a + decrease in <hi rend="italic">enjoyment</hi> after + a peak in chapter 4.</p> + <p>A similar study by Schmidt and Burghardt<note + type="footnote"> + <ref type="bibliography" target="#schmidt_evaluation_2018">Schmidt / Burghardt 2018</ref>.</note> + also works on German text – but focuses on the + mostly neglected domain of theater plays, more + concretely the plays by Lessing. They perform an + annotation study and subsequently analyze + different established emotion lexicons to recover + the emotion automatically. The configuration of + the best performing system shows the highest + accuracy of 0.7, while a majority baseline obtains + 0.695.</p> + <p>Yet another work that tracks the flow of + emotions in a collection of texts is presented by + Kim et al.<note type="footnote"> <ref type="bibliography" target="#kim_emotion_2017">Kim et al. + 2017b</ref>.</note> The authors hypothesize that + literary genres can be linked to the development + of emotions over the course of text. To test this, + they collect more than 2,000 books from five + genres (<hi rend="italic">adventure</hi>, <hi + rend="italic">science fiction</hi>, <hi + rend="italic">mystery</hi>, <hi rend="italic" + >humor</hi> and <hi rend="italic">romance</hi>) + from Project Gutenberg and identify prototypical + emotion shapes for each genre. Each novel in the + corpus is split into five consecutive + equally-sized segments (following the five-act + theory of dramatic acts).<note type="footnote"> + <ref type="bibliography" target="#freytag_technik_1863">Freytag + 1863</ref>.</note> All five genres show close + correspondence with regard to <hi rend="italic" + >sadness</hi>, <hi rend="italic">anger</hi>, <hi + rend="italic">fear</hi> and <hi rend="italic" + >disgust</hi>, i.e., a consistent increase of + these emotions from Act 1 to Act 5, which may + correspond to an entertaining narrative. <hi + rend="italic">Mystery</hi> and <hi rend="italic" + >science fiction</hi> books show increase in <hi + rend="italic">anger</hi> towards the end, and <hi + rend="italic">joy</hi> shows an inverse decreasing + pattern from Act 1 to Act 2, with the exception of + <hi rend="italic">humor</hi>.</p> + <p>The work by Kakkonen and Galic Kakkonen<note + type="footnote"> <ref type="bibliography" target="#kakkonen_profiles_2011">Kakkonen / + Galic Kakkonen 2011</ref>.</note> aims at supporting the literary + analysis of <hi rend="italic">Gothic</hi> texts at + the sentiment level. The authors introduce a + system called <term type="dh">SentiProfiler</term> + that generates visual representations of affective + content in such texts and outlines similarities + and differences between them, however, without + considering the temporal dimension. The <term + type="dh">SentiProfiler</term> uses <term + type="dh">WordNet-Affect</term> to derive a list + of emotion-bearing words that will be used for + analysis. The resulting sentiment profiles for the + books are used to visualize the presence of + sentiment in a particular document and to compare + two different texts. </p> + </div> + <div type="subchapter"> + <head>4.5.2 Miscellaneous</head> + + <p>In this section, we review studies that are + different in goals and research questions from the + papers presented in previous sections and do not + constitute a category on their own. </p> + <p>Koolen<note type="footnote"> <ref type="bibliography" target="#koolen_books_2018">Koolen 2018</ref>.</note> claims that there is a bias among + readers that put works by female authors on par + with »women’s books«, which, as stated by the + author, tend to be perceived as of lower literary + quality. She investigates how much »women’s books« + (here, <hi rend="italic">romantic</hi> novels + written by women) differ from novels perceived as + literary (female and male-authored literary + fiction). The corpus used in the study is a + collection of European and North-American novels + translated into Dutch. Koolen uses a Dutch version + of the <bibl> + <title type="desc">Linguistic Inquiry</title> + </bibl> and <term type="dh">Word + Count</term>,<note type="footnote"> <ref type="bibliography" target="#boot_translation_2017">Boot et al. + 2017</ref>.</note> a dictionary that contains content + and sentiment-related categories of words to count + the number of words from different categories in + each type of fiction. Her analysis shows that + romantic novels contain more positive emotions and + words pertaining to friendship than in literary + fiction. However, female-authored literary novels + and male-authored ones do not significantly differ + on any category. </p> + <p>Kraicer and Piper<note type="footnote"> <ref type="bibliography" target="#kracier_characters_2019">Kraicer / + Piper 2019</ref>.</note> explore the women’s place + within contemporary fiction starting from the + premise that there is a near ubiquitous + underrepresentation and decentralization of women. + As a part of their analysis, Kraicer and Piper use + sentiment scores to look at social balance and + »antagonism«, i.e., how different gender pairings + influence positive and negative language + surrounding the co-occurrence of characters (using + the sentiment dictionary presented by Liu<note + type="footnote"> <ref type="bibliography" target="#liu_analysis_2010">Liu et al. + 2010</ref>.</note> to + calculate a sentiment score for a character pair). + Having analyzed a set of 26,450 characters from + 1,333 novels published between 2001 and 2015, the + authors find that sentiment scores give little + indication that the character’s gender has an + effect on the state of social balance.</p> + <p>Morin and Acerbi<note type="footnote"> <ref type="bibliography" target="#morin_birth_2017">Morin / Acerbi + 2017</ref>.</note> focus on larger-scale data + spanning a hundred thousand of books. The goal of + their study is to understand how emotionality of + written texts changed throughout the centuries. + Having collected 307,527 books written between + 1900 and 2000 from the <ref + target="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html" + >Google Books corpus</ref><note type="footnote"> + <ref type="bibliography" target="#google_books_2012">Google Books Ngram + Viewer 2012</ref>.</note> they + collect, for each year, the total number of + case-insensitive occurrences of emotion terms that + are found under positive and negative taxonomies + of <term type="dh">LIWC</term> dictionary.<note + type="footnote"> <ref type="bibliography" target="#pennebaker_development_2007">Pennebaker + et al. 2007</ref>.</note> + The main findings of their research show that + emotionality (both <hi rend="italic">positive</hi> + and <hi rend="italic">negative</hi> emotions) + declines with time, and this decline is driven by + the decrease in usage of positive vocabulary. + Morin and Acerbi remind us that the <hi + rend="italic">Romantic</hi> period was dominated + by emotionality in writing, which could be the + effect of a group of writers who wrote above the + mean. If one assumes that each new writer tends to + copy the emotional style of their predecessors, + then writers at one point of time are + disproportionally influenced by this group of + above-the-mean writers. However, this trend does + not last forever and, sooner or later, the trend + reverts to the mean, as each writer reverts to a + normal level of emotionality.</p> + <p>An earlier work<note type="footnote"> <ref type="bibliography" target="#bentley_books_2014">Bentley et al. + 2014</ref>.</note> written in collaboration with + Acerbi provides a somewhat different approach and + interpretation of the problem of the decline in + positive vocabulary in English books of the + twentieth century. Using the same dataset and + lexical resources (plus <term type="dh" + >WordNet-Affect</term>) Bentley et al. find a + strong correlation between expressed negative + emotions and the <term type="dh">U.S. economic + misery index</term>, which is especially strong + for the books written during and after the World + War I, the Great Depression, and the energy crisis + in the 1970s. However, in the present study,<note + type="footnote"> <ref type="bibliography" target="#morin_birth_2017">Morin / Acerbi + 2017</ref>.</note> the + authors argue that the extent to which positive + emotionality correlates with subjective well-being + is a debatable issue. Morin and Acerbi provide + more possible reasons for this effect as well as + detailed statistical analysis of the data, so we + refer the reader to the original paper for more + information.</p> + + <figure> + <graphic xml:id="emotion_analysis_2019_003" + url=".../medien/emotion_analysis_2019_003.png"> + <desc> + <ref target="#abb3">Tab. 1</ref>: Summary of characteristics of + methods used in the papers reviewed in this survey. Download as <ref + target="http://www.zfdg.de/files/table_zfdg_klinger.pdf">PDF</ref>. + [Kim / Klinger 2021]<ref type="graphic" + target="#emotion_analysis_2019_003"/> + </desc> + </graphic> + </figure> + </div> + </div> + </div> + <div type="chapter"> + <head>5 Discussion and Conclusion</head> + + <p>We have shown throughout this survey that there is a growing interest in + sentiment and emotion analysis within computational literary studies as one + main field of digital humanities. Given the fact that DH have emerged into a + thriving science within the past decade, it may safely be said that this + direction of research is relatively new. It further constitutes an + interesting field that connects literary studies and computational + linguistics. </p> + <p>In computational linguistics, sentiment analysis started more than two + decades ago and is nowadays an established field that has dedicated + workshops and tracks in the main conferences. Moreover, a recent meta-study + by Mäntylä et al.<note type="footnote"> <ref type="bibliography" target="#maentylae_evolution_2018">Mäntylä et al. + 2018</ref>.</note> shows + that the number of papers in sentiment analysis is rapidly increasing each + year. Indeed, the topic has not yet outrun itself and we should not expect + to see it vanishing within the next decade or two. In addition, there are + still many open challenges. For each novel representation-learning approach, + the question arises how sentiment concepts can be approprietly included. For + most languages in the world the number of resources is low and it is not + even known if established approaches could simply be transferred. To + leverage these issues, research on multilingual methods that induce models + in resource-scarce environments is an interesting modern direction, and a + promising and rewarding field. All these developments on machine learning + models, domain adaptation, pretraining and fine-tuning will also be + beneficial for the digital humanities, but we cannot expect that all + particular challenges that arise from research questions in literary studies + will be solved in this field that focuses on generalizable methods.</p> + <p>Digital humanties has specific needs that cannot be readily addressed by + existing methods or those that are developed in the future, in computational + linguistics, machine learning, and computer science in general. As we have + seen in this survey, most of the works rely on affective lexicons and word + counts, a technique for detecting emotions in literary text first used by + Anderson and McMaster in 1982.<note type="footnote"> <ref type="bibliography" target="#anderson_computer_1982">Anderson / McMaster + 1982</ref>.</note> Even the most recent works base the interpretation of the + results on the use of dictionaries and counts of emotion-bearing words in a + text, passage, or sentence. In fact, around 70 % of the papers we discussed + + in <ref type="intern" target="#hd10">section 4</ref> substantially rely on the + + use of various lexical resources for detecting emotions. We identify a set + of particular challenges that hold for digital humanities and computational + literary studies and that are presumable reasons for that choice. </p> + <p> + <hi rend="italic">The object of research is the central element. </hi>In + contrast to computational linguistics, the goal of digital humanities is not + to develop generalizable methods. The goal is, instead, to develop those + methods that are helpful for a particular research question; and in contrast + to computational linguistics, this includes tasks that only very few people + work on. It would be a huge advantage if those methods could be generalized + and reused, however, it is not a primary goal. Instead, an emotion analysis + method for a particular scholar who analyzes texts from a particular subset, + for instance genre, period, or author needs to work well for this subset. It + might not be feasable to develop sophisticated deep learning methods for + each of these approaches, but just to be used once.</p> + <p> + <hi rend="italic">Transparency of the computational method is not a bonus; + it is a crucial property.</hi> In digital humanities, research is often + exploratory. The application of an existing method on a corpus can lead to + new findings, but it is common that an interactive application of a method + to explore a phenomenon is even more promising. Such interactive application + requires full control by the user in real time – and that is something that + pretrained deep neural methods cannot (yet) provide. However, emotion + lexicons that point to particular aspects in the text in a transparent + manner do, despite of their disadvantages.</p> + <p> + <hi rend="italic">Computational expertise is not sufficient in an + interdisciplinary research field. </hi>In computational research + disciplines, a minimum amount of understanding of the respective domain is + helpful but not necessarily (always) required. Particularly in recent years, + with the development of end-to-end learning methods that hardly explain + decisions, it became common to purely rely on performance measures (though + this changes with recent research on explainable artificial intelligence). + In contrast, in computational literary studies, knowledge of the domain is + required. Without it, research questions cannot be answered. This is not a + unique property of digital humanities as an interdisciplnary field. However, + it is particularly challenging here, given its recent growth, fast + development, and also the differences in the research culture between + humanities and computer science (which are arguably smaller between, for + instance, natural sciences and computer science, to which fields like + computational chemistry or bioinformatics belong).</p> + <p>This leads to a set of challenges that need to be addressed, while developing + methods further. In contrast to most emotion analysis work in other domains + (like social media or news), the unit of analysis should be larger. It is + not sufficient to only analyze sentences in isolation (or even just words). + Instead, the overall development of characters, the story line as a whole + need to be considered. This is a research direction that hardly received any + attention yet; presumably because of technical challenges, but likely also + due to the lack of annotated corpora that would be required to contain + annotations on different levels. Further, these annotations need particular + expertise from the annotators. It is not feasible to show an entire book to + workers on a crowdsourcing platform to receive annotations on fine-grained + levels (for characters and their developments). Therefore, for domains of + interest, we point out that the development of corpora in computational + literary studies are expected to be more expensive and will take longer than + in other fields in which emotion analysis is applied.</p> + <p>Finally, we believe that the integration of psychological models into + computational approaches in literature studies is important. Literature + contains representations of whole worlds, the depictions are more + comprehensive than in news articles or social media. This also requires a + deeper understanding of described social processes and (imagined) mental + states.</p> + <p>And finally, the role of the experiencer of an emotion needs to be considered + more than in other fields. While on Twitter analysis, we typically care + about the emotion that the author of a message felt while writing it, we + typically do not care about the emotion of the author of a novel, while + writing it.<note type="footnote"> <ref type="bibliography" target="#oberlaender_experiencers"> + Oberländer et al. 2020</ref>.</note> Instead, we + are faced with the more challenging task to attribute emotions to characters + or even infer the emotions that might be developed by readers of a text.</p> + <p>In summary, we believe that the field of emotion analysis for literary + studies has still space for research in multiple directions. The main + challenge will be to identify the particular challenges of literare and + develop methods for these text genres, instead of using existing methods + that have developed with the purpose in mind of being generalizing across + application areas.</p> + </div> + <div type="chapter"> + <head>Acknowledgements</head> + + <p>We thank Laura Ana Maria Oberländer, Sebastian Padó, and Enrica Troiano for + fruitful discussions and the ZfDG team for their help in preparation of this + article. This research has been conducted within the <ref + target="www.creta.uni-stuttgart.de\" + >CRETA</ref> project which is funded by the German Ministry for + Education and Research (BMBF) and partially funded by the German Research + Council (DFG), projects SEAT (Structured Multi-Domain Emotion Analysis from + Text, KL 2869/1-1). 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[<ref + type="bibliography" target="#plutchik_wheel_2011">Plutchik 2011</ref>. <ref + target="https://creativecommons.org/publicdomain/mark/1.0/deed.de" + >PD</ref>]<ref type="graphic" target="#emotion_analysis_2019_001"/></desc> + + <desc type="graphic" xml:id="abb2">Circumplex model of affect: Horizontal axis + represents the valence dimension, the vertical axis represents the arousal + dimension. Drawn after <ref type="bibliography" target="#posner_model_2005">Posner + et al. 2005</ref>. [Kim / Klinger 2019]<ref type="graphic" + target="#emotion_analysis_2019_002"/></desc> + + <desc type="graphic" xml:id="abb3">Summary of characteristics of methods used in the + papers reviewed in this survey. Download as <ref + target="http://www.zfdg.de/files/table_zfdg_klinger.pdf">PDF</ref>. [Kim / + + Klinger 2021]<ref type="graphic" target="#emotion_analysis_2019_003"/></desc> + </div> + </div> + + </body> + </text> +</TEI>