In theatrical pieces, written language is the primary medium for establishing antagonisms. As one of the most important figures of renaissance, Shakespeare wrote characters which express themselves clearly. Thus, the emotional landscape of the plays can be revealed from the texts. It is important to analyze such landscapes for further demonstrating these structures. We use word-emotion association lexicon with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). By using this lexicon, the emotional state of each character is represented in 10 dimensional space and mapped onto a plane. This principle axes planes position each character relatively. Additionally, tempora-emotional evaluation of each play is graphed. We conclude that the protagonist and the antagonist have different emotional states from the rest and these two emotionally oppose each other. Temporal-Emotional timeline of the plays are meaningful to have a better insight into the tragedies.
Shakespeare’s plays are one of the most important works of early modernity with their dramaturgy, strong and in-depth characters and poems, that are all still contemporary. Antagonistically most powerful works of Shakespeare, who wrote in three genres, are tragedies. Tragedies have strong antagonisms and written language is the primary
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). medium for establishing antagonisms. Therefore, the characters in these plays express themselves through dialogues or monologues clearly. By using the emotional association of each word, from this perspective, it is possible to reveal the emotional landscape of the plays. Emotionally positioning the characters relative to each other is important for further understanding of the structure of the plays. It is also important to extract overall emotional variations throughout the play to have an insight about the tragedies. In this study, it is aimed to answer these two questions by using word-emotion association lexicon with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive).
These tasks are not only important for
artificial literature
In order to convey such analyses, each character in any play represented in 10 dimensional emotional space and mapped onto a plane. The emotional timeline of the plays are also revealed. We would like to overview the word-emotion association lexicon and dimension reduction algorithm. Then, the last section is left to discussions on the emotional states of the characters and temporal analyses of the emotions. 1.1
Distant reading is a established
approach/methodology in digital humanities
(DH) and mostly deals with the quantitative
analyses of literary and cultural studies
Our approach is lexicon based. Lines uttered by each character treated as a document and represented with a tf-idf. Emotional weights are multiplied 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 represented in relative point to each other. Lexicon is also used to extract temporal emotional variations along the texts. 2.1
Psychologists proposed many theories for
classifying human emotions,
There are theoretical studies on what the
basic emotions are. Ekman argues about about
the existence of 6 basic emotions: joy, sadness,
anger, fear, disgust, and surprise,
Emolex is the dictionary of English words
and their associations with eight basic emotions
(anger, fear, anticipation, trust, surprise, sadness,
joy, and disgust) and two sentiments (negative and
positive),
A = U SV 0 which
U U 0 = I and V V 0 = I S is a diagonal matrix that consists of r singular values. r is the rank of A. Truncated SVD is a reduced rank approximation. Only the most relevant dimensions are selected, these are the largest singular values. The dimensions of truncated SVD are [uxk] [kxk] [kxv] Therefore A matrix is approximated by k dimensions, this is the dimension reduction. A descriptive subset of the data is called T, which is a dense summary of the matrix A, (1) (2) (3)
T = U Sk Sk denotes k largest singular values, which is the number of reduced features. Each feature is represented with a percentage of variance. Higher variance means more information gain. 3
There are two analyzes we would like to discuss. The first is about whether we can get ideas about the plays based on the emotional state of the characters. For this purpose, each character is treated as a text and represented with Tf-Idf features. The weights of each word for a specific character multiplied with its 10-dimensional emotion vector. Right afterwards, SVD lets you extract principle axes and mapped onto the plane. This is the first analysis to be discussed. Secondly, the temporal dimensions of the plays are considered. The opposing emotional pairs (given by Plutchik) were represented as a time series as positive / negative. The states are represented as a cumulative temporal sum and the emotional landscape of the tragedies are revealed.
The weights of Tf-Idf features help to position character emotions. Each word contributes to the resulting emotion with their weights. Weights of more frequent terms affect the resulting emotional state more, while less frequent terms affect less. The 10-dimensional emotional space mapped onto an abstraction plane by linear dimension reduction. The dimensions of these planes correspond to abstract emotions or a mixture of the other 10 dimensions with certain proportions. The important thing in these graphics is the position of the characters relative to each other. The emotional positioning of Hamlet in the upper left, Othello in the upper right, Romeo and Juliet in the lower left, and Macbeth in the lower right.
In these graphics, the basic characteristics of the plays can be observed. The main characters or pairs of characters are emotionally different from the rest. The protagonist and the antagonist always have emotional contrast. For example, the Hamlet play is basically determined by the tension between the two people, Hamlet and King Claudius. Although Hamlet is emotionally very different from other characters, King Claudius is emotionally close to the main character cluster. In all tragedies, there is a cluster of emotionally indifferent characters, we can call the main cluster. Characters like Lord Polonius and Laertes are also located around King Claudius with the main cluster. The Ghost character, like Hamlet, is different from all other characters and is in an opposite position to Hamlet. These observations follow the readings of the play. In Othello, Iago sets traps to harm Desdemona. Desdemona is also compatible with the main cluster. But Iago and Othello are positioned far apart and apart from the main cluster. In the Macbeth play, the two enemies, Macduff and Malcolm, are opposite and separately positioned. Lady Macbeth is emotionally compatible with the main cluster. In an interesting observation on Romeo and Juliet, the positioning of the clusters is placed in symmetry in accordance with Renaissance thought. It is known that the play is written symmetrically. There are three family positions in symmetry: Ruling house of Verona, House of Capulet, House of Montague.
The graphs show that the emotional positioning of tragedies is compatible with the readings of the play. What we mean is the protagonists and antagonists are clearly observable. Distances or orientations, or rather relative positions, are significant. The main characters that experience basic tensions could be demonstrated. In the play of Romeo and Juliet, the affinities are observed and there is symmetrical positioning of the families. 3.2
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 directions are determined. Emotions are positioned in contrast as (Negative-Positive), (Fear-Anger), (Anticipation-Surprise), (Sadness-Joy), (DisgustTrust). For each timestep, or the word, the contribution is the expected emotion,
E[e] = e p(e) (4) which p(e) is the occurrence probability of the emotion in the lexicon and e is the Bernoulli random variable, either 0 or 1, either has the emotion or not. The cumulative sum of a emotional contrast pair,
T C(fe1; e2g; T ) = X t=0
E[e2; t]
E[e1; t] (5) which fe1; e2g are the random variables for emotional contrast pairs. The cumulative total for each pair is specified for 5 curves. Cumulative sums of (Negative-Positive), (Sadness-Joy), (Disgust-Trust) pairs for all four plays constantly increases. (Fear-Anger), (Anticipation-Surprise) are more neutral. All in all emotionally, the temporal word distributions for tragedies are similar. 4
The traces of the tensions between characters are observable from the emotional aspect. As we show, the emotional positions of the protagonist, the antagonist and the main cluster gives much insight about the greatness such pieces. Any great tragedy needs emotional contrast between the main characters and there is always the main cluster. The temporal-emotional characteristics of the plays are also important and very much similar to each other. There are constantly increasing emotions as well as neutrals. Each play grows towards positive, joyful and trusty emotional state. This might be the reason behind a followable play. Positive feelings should accumulate.
Either it is computer-assisted or fully automated writing machine, artificial literature needs emotional aspect. The emotional aspect of literary works should be conditional of such generative models. The common acceptance on this early modern author is his greatness as a tragedy writer. The theatrical pieces by Shakespeare are in dialog form, each character express themselves clearly. Therefore, it is shown that any dramatic antagonism is also emotional. Any artificial dramatic work should have a similar emotional resonance with such tragedies. With this analyses, we try to further develop evaluation metrics for artificial literature. A baseline metric to emotionally evaluate such theatrical forms.
On the way to Artificial Literature (ALit), there
needs more criteria and more complex tools to
analyze literariness of such pieces