Analyses of Character Emotions in Dramatic Works by Using EmoLex Unigrams Mehmet Can Yavuz Faculty of Engineering and Natural Science, Sabancı University, Tuzla İstanbul, Türkiye mehmetyavuz@sabanciuniv.edu Abstract medium for establishing antagonisms. Therefore, the characters in these plays express themselves In theatrical pieces, written language is through dialogues or monologues clearly. By us- the primary medium for establishing an- ing the emotional association of each word, from tagonisms. As one of the most impor- this perspective, it is possible to reveal the emo- tant figures of renaissance, Shakespeare tional landscape of the plays. Emotionally posi- wrote characters which express them- tioning the characters relative to each other is im- selves clearly. Thus, the emotional land- portant for further understanding of the structure scape of the plays can be revealed from of the plays. It is also important to extract overall the texts. It is important to analyze such emotional variations throughout the play to have landscapes for further demonstrating these an insight about the tragedies. In this study, it structures. is aimed to answer these two questions by using We use word-emotion association lexicon word-emotion association lexicon with eight ba- with eight basic emotions (anger, fear, an- sic emotions (anger, fear, anticipation, trust, sur- ticipation, trust, surprise, sadness, joy, and prise, sadness, joy, and disgust) and two senti- disgust) and two sentiments (negative and ments (negative and positive). positive). By using this lexicon, the emo- These tasks are not only important for artifi- tional state of each character is represented cial literature (Lebrun, 2017; Yavuz, 2020), but in 10 dimensional space and mapped onto also for the purpose of increasing artistic cre- a plane. This principle axes planes posi- ativity through computerized analysis. The al- tion each character relatively. Addition- gorithmic study of literary works has given rise ally, tempora-emotional evaluation of each to objective criticism in literary theory, (Moretti, play is graphed. 2013). Accordingly, the discovery of new or We conclude that the protagonist and the previously theoretically laid out features of lit- antagonist have different emotional states erary texts through mathematical experiments or from the rest and these two emotionally statistics brought along conjunctions on literature, oppose each other. Temporal-Emotional (Moretti, 2000; Yavuz, 2020). Art and criticism timeline of the plays are meaningful to develop in parallel today as of yesterday. Com- have a better insight into the tragedies. putational linguistics enriched with fields such as advanced chatbots, conversational AI, or text style 1 Introduction transfers, the fields give clues that artificial liter- Shakespeare’s plays are one of the most important ature will also develop rapidly in the upcoming works of early modernity with their dramaturgy, period. Whether it is a computer-assisted writ- strong and in-depth characters and poems, that are ing process or fully automated writing machines, all still contemporary. Antagonistically most pow- these generative models need evaluation metrics. erful works of Shakespeare, who wrote in three For this purpose, objective metrics should be de- genres, are tragedies. Tragedies have strong an- veloped for outstanding literary pieces such as tagonisms and written language is the primary Shakespeare, and for what makes these plays so great, if possible. . . Aim of this paper is in ac- Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 cordance with the previous works, (Yavuz, 2019; International (CC BY 4.0). Yavuz, 2020), we would like to discuss objective and quantifiable reasons behind the literariness of tions along the texts. a drama. 2.1 EmoLex Unigrams In order to convey such analyses, each charac- ter in any play represented in 10 dimensional emo- Psychologists proposed many theories for classi- tional space and mapped onto a plane. The emo- fying human emotions, (Dalgleish, 2000). Some tional timeline of the plays are also revealed. We emotions are considered basic, while others are would like to overview the word-emotion associ- considered as complex. The distinction can be ation lexicon and dimension reduction algorithm. between emotions that we can sense and per- Then, the last section is left to discussions on the ceive (instinctual), and emotions that can arrive af- emotional states of the characters and temporal ter some thinking and reasoning (cognitive), (Za- analyses of the emotions. jonc,1984). There are also oppositions, (Lazarus, 1984). According to Plutchik, (Plutchick, 1985), 1.1 Related Works the discussion may not be resolvable because there Distant reading is a established ap- is no empirical basis. There is a high correlation proach/methodology in digital humanities between basic and instinctive emotions, similarly (DH) and mostly deals with the quantitative between complex and cognitive emotions. Many analyses of literary and cultural studies (Clement, of the basic emotions are also instinctual. 2008;Crane, 2006). Inside DH, ”Drametrics” is There are theoretical studies on what the ba- a sub-research field, specialized on quantitative sic emotions are. Ekman argues about about analysis of the literary genre of drama (Romanska, the existence of 6 basic emotions: joy, sadness, 2015). Digital Shakespeare research and projects anger, fear, disgust, and surprise, (Ekman, 1992). have gotten attention since the 2000s (Hirsch, Plutchik includes two new emotions: trust and an- 2014; Mueller, 2008). The dramatic structures ticipation, (Plutchick, 1994). Plutchik displayed in the form of antagonisms are revealed by topic the emotions on a wheel. The distance from the modeling algorithms, (Yavuz, 2019). Exceptional center in the circle indicates intensity. Plutchik characters, such as Deus-ex-Machina, is also also state the basic emotions as opposing pairs: detected in semantic space, (Yavuz, 2020). The joy–sadness, anger–fear, trust–disgust, and antic- graphs are also used to extract secondary antago- ipation–surprise. The opposing places and neigh- nisms, (Yavuz,2020). Machine learning based text borhoods on the circle are formed accordingly. analyses are also carried out for genre classifica- Emolex is the dictionary of English words tions (Yavuz, 2019; Ardanuy, 2014; Hope, 2010; and their associations with eight basic emotions Schöch, 2016; Underwood, 2013; Yu, 2008). In (anger, fear, anticipation, trust, surprise, sadness, literature, structural elements such as dramatis joy, and disgust) and two sentiments (negative and persone are also analyzed and applications are developed for further analyses (Dennerlein, 2015; Krautter, 2018; Schmidt, 2019, Trilcke, 2015; Wilhelm, 2013). In addition to dramatic structure works, there is literature that apply sentiment analyses for dramatic works, (Nalisnick, 2013; Schmidt, 2018; Schmidt, 2018a; Schmidt, 2018b). 2 Methodology Our approach is lexicon based. Lines uttered by each character treated as a document and repre- sented with a tf-idf. Emotional weights are multi- plied with the vectors and the summed up to have the final 10 dimensional emotion space and then reduced to a plane by linear dimension reduction. Overall emotional state of any character, thus, is Figure 1: Eight basic emotions (anger, fear, antic- represented in relative point to each other. Lexicon ipation, trust, surprise, sadness, joy, and disgust) is also used to extract temporal emotional varia- on Plutchik’s Wheel of Emotions positive), (Mohammad, 2013). Thus, represent- The opposing emotional pairs (given by Plutchik) ing each word in 10 dimensional emotion space were represented as a time series as positive / neg- is effective as well as theoretically relevant. The ative. The states are represented as a cumulative dataset is unigram. Each single word represented temporal sum and the emotional landscape of the in 10 dimensional feature space: 8 basic emotions tragedies are revealed. and 2 sentiment labeling. Each emotion can be either 0 or 1. No intensity information is given. 3.1 Analyses of Character Emotions The words are crowd-sourced and manually la- beled with Mechanical Turk. There are 14182 un- igrams in the dictionary. 2.2 SVD (Golub, 1970) Linear dimension reduction methods are for get- ting a n-dimensional plane over the hyperspace. For instance, if you have data cloud in 10 dimen- sion, by mapping onto a plane, one can visualize such points. SVD is one of the relevant methods, it breaks any A matrix into three, A = U SV 0 which (1) 0 0 U U = I and V V = I (2) S is a diagonal matrix that consists of r singular values. r is the rank of A. Truncated SVD is a re- duced rank approximation. Only the most relevant dimensions are selected, these are the largest sin- gular values. The dimensions of truncated SVD Figure 2: Emotional position of each character is are [uxk] ∗ [kxk] ∗ [kxv] Therefore A matrix is represented, Hamlet, Othello, Romeo and Juliet, approximated by k dimensions, this is the dimen- Macbeth, respectively. sion reduction. A descriptive subset of the data is called T, which is a dense summary of the matrix The weights of Tf-Idf features help to position A, character emotions. Each word contributes to the resulting emotion with their weights. Weights of T = U Sk (3) more frequent terms affect the resulting emotional state more, while less frequent terms affect less. Sk denotes k largest singular values, which is the The 10-dimensional emotional space mapped onto number of reduced features. Each feature is repre- an abstraction plane by linear dimension reduc- sented with a percentage of variance. Higher vari- tion. The dimensions of these planes correspond ance means more information gain. to abstract emotions or a mixture of the other 10 dimensions with certain proportions. The impor- 3 Discussion tant thing in these graphics is the position of the There are two analyzes we would like to discuss. characters relative to each other. The emotional The first is about whether we can get ideas about positioning of Hamlet in the upper left, Othello in the plays based on the emotional state of the char- the upper right, Romeo and Juliet in the lower left, acters. For this purpose, each character is treated and Macbeth in the lower right. as a text and represented with Tf-Idf features. In these graphics, the basic characteristics of the The weights of each word for a specific character plays can be observed. The main characters or multiplied with its 10-dimensional emotion vec- pairs of characters are emotionally different from tor. Right afterwards, SVD lets you extract prin- the rest. The protagonist and the antagonist al- ciple axes and mapped onto the plane. This is ways have emotional contrast. For example, the the first analysis to be discussed. Secondly, the Hamlet play is basically determined by the ten- temporal dimensions of the plays are considered. sion between the two people, Hamlet and King Claudius. Although Hamlet is emotionally very or not. The cumulative sum of a emotional con- different from other characters, King Claudius is trast pair, emotionally close to the main character cluster. In T   all tragedies, there is a cluster of emotionally in- X C({e1 , e2 }, T ) = E[e2 , t] − E[e1 , t] (5) different characters, we can call the main cluster. t=0 Characters like Lord Polonius and Laertes are also located around King Claudius with the main clus- which {e1 , e2 } are the random variables for emo- ter. The Ghost character, like Hamlet, is different tional contrast pairs. The cumulative total for from all other characters and is in an opposite po- each pair is specified for 5 curves. Cumula- sition to Hamlet. These observations follow the tive sums of (Negative-Positive), (Sadness-Joy), readings of the play. In Othello, Iago sets traps to (Disgust-Trust) pairs for all four plays constantly harm Desdemona. Desdemona is also compatible increases. (Fear-Anger), (Anticipation-Surprise) with the main cluster. But Iago and Othello are are more neutral. All in all emotionally, the tem- positioned far apart and apart from the main clus- poral word distributions for tragedies are similar. ter. In the Macbeth play, the two enemies, Mac- duff and Malcolm, are opposite and separately po- 4 Conclusion sitioned. Lady Macbeth is emotionally compatible The traces of the tensions between characters are with the main cluster. In an interesting observa- observable from the emotional aspect. As we tion on Romeo and Juliet, the positioning of the show, the emotional positions of the protagonist, clusters is placed in symmetry in accordance with the antagonist and the main cluster gives much Renaissance thought. It is known that the play insight about the greatness such pieces. Any is written symmetrically. There are three family great tragedy needs emotional contrast between positions in symmetry: Ruling house of Verona, the main characters and there is always the main House of Capulet, House of Montague. cluster. The temporal-emotional characteristics of The graphs show that the emotional positioning the plays are also important and very much simi- of tragedies is compatible with the readings of the lar to each other. There are constantly increasing play. What we mean is the protagonists and antag- emotions as well as neutrals. Each play grows to- onists are clearly observable. Distances or orien- wards positive, joyful and trusty emotional state. tations, or rather relative positions, are significant. This might be the reason behind a followable play. The main characters that experience basic tensions Positive feelings should accumulate. could be demonstrated. In the play of Romeo and Either it is computer-assisted or fully automated Juliet, the affinities are observed and there is sym- writing machine, artificial literature needs emo- metrical positioning of the families. tional aspect. The emotional aspect of literary works should be conditional of such generative 3.2 Temporal-Emotional Evaluation of the models. The common acceptance on this early Tragedies Temporal-emotional evaluation of the tragedies are drawn. We can assume that each play is a temporal series, and we summed up the emotional state in each timestep, the cumulative emotional curves are calculated. Therefore, the emotional di- rections are determined. Emotions are positioned in contrast as (Negative-Positive), (Fear-Anger), (Anticipation-Surprise), (Sadness-Joy), (Disgust- Trust). For each timestep, or the word, the contri- bution is the expected emotion, E[e] = e ∗ p(e) (4) which p(e) is the occurrence probability of the Figure 3: Emotional landscape of each character emotion in the lexicon and e is the Bernoulli ran- is represented, Hamlet, Othello, Romeo and Juliet, dom variable, either 0 or 1, either has the emotion Macbeth, respectively. modern author is his greatness as a tragedy writer. 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