acl acl2013 acl2013-79 knowledge-graph by maker-knowledge-mining

79 acl-2013-Character-to-Character Sentiment Analysis in Shakespeare's Plays


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Author: Eric T. Nalisnick ; Henry S. Baird

Abstract: We present an automatic method for analyzing sentiment dynamics between characters in plays. This literary format’s structured dialogue allows us to make assumptions about who is participating in a conversation. Once we have an idea of who a character is speaking to, the sentiment in his or her speech can be attributed accordingly, allowing us to generate lists of a character’s enemies and allies as well as pinpoint scenes critical to a character’s emotional development. Results of experiments on Shakespeare’s plays are presented along with discussion of how this work can be extended to unstructured texts (i.e. novels).

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 of Computer Science and Engineering Lehigh University Bethlehem, PA 18015, USA {etn2 12 ,hsb2 } @ lehigh . [sent-4, score-0.051]

2 edu Abstract We present an automatic method for analyzing sentiment dynamics between characters in plays. [sent-5, score-0.495]

3 This literary format’s structured dialogue allows us to make assumptions about who is participating in a conversation. [sent-6, score-0.14]

4 Once we have an idea of who a character is speaking to, the sentiment in his or her speech can be attributed accordingly, allowing us to generate lists of a character’s enemies and allies as well as pinpoint scenes critical to a character’s emotional development. [sent-7, score-0.6]

5 Results of experiments on Shakespeare’s plays are presented along with discussion of how this work can be extended to unstructured texts (i. [sent-8, score-0.117]

6 1 Introduction Insightful analysis of literary fiction often challenges trained human readers let alone machines. [sent-11, score-0.222]

7 In fact, some humanists believe literary analysis is so closely tied to the human condition that it is impossible for computers to perform. [sent-12, score-0.169]

8 In his book Reading Machines: Toward an Algorithmic Criticism, Stephen Ramsay (201 1) states: Tools that can adjudicate the hermeneutical parameters of human reading experiences. [sent-13, score-0.042]

9 Antonio Roque (2012) has challenged Ramsay’s claim, and certainly there has been successful work done in the computational analysis and modeling of narratives, as we will review in the next section. [sent-17, score-0.029]

10 However, we believe that most previous work (except possibly (Elsner, 2012)) has failed to directly address the root cause of Ramsay’s skepticism: can computers extract the emotions encoded in a narrative? [sent-18, score-0.119]

11 For example, can the love that Shakespeare’s Juliet feels for Romeo be computationally tracked? [sent-19, score-0.044]

12 Empathizing with characters along their journeys to emotional highs and lows is often what makes a narrative compelling for a reader, and therefore we believe mapping these journeys is the first step in capturing the human reading experience. [sent-20, score-0.464]

13 Unfortunately but unsurprisingly, computational modeling of the emotional relationships described in natural language text remains a daunting technical challenge. [sent-21, score-0.154]

14 The reason this task is so difficult is that emotions are indistinct and often subtly conveyed, especially in text with literary merit. [sent-22, score-0.193]

15 Humans typically achieve no greater than 80% accuracy in sentiment classification experiments involving product reviews (Pang et al. [sent-23, score-0.389]

16 Similar experiments on fiction texts would presumably yield even higher error rates. [sent-25, score-0.086]

17 In order to attack this open problem and make further progress towards refuting Ramsay’s claim, we turn to shallow statistical approaches. [sent-26, score-0.039]

18 In the following paper, we describe our attempts to use modern sentiment lexicons and dialogue structure to algorithmically track and model–with no domain-specific customization–the emotion dynamics between characters in Shakespeare’s plays. [sent-28, score-0.68]

19 1 2 Sentiment Analysis and Related Work Sentiment analysis (SA) is now widely used commercially to infer user opinions from product reviews and social-media messages (Pang and Lee, 1XML versions provided by Jon http://www. [sent-29, score-0.058]

20 IMDB’s user reviews are labeled with one to ten stars, which are assumed to correlate with the text’s polarity) (Pang et al. [sent-36, score-0.029]

21 These methods are driven by sentiment lexicons, fixed lists associating words with “valences” (signed integers representing positive and negative feelings) (Kim and Hovy, 2004). [sent-40, score-0.454]

22 Some lexicons allow for analysis of specific emotions by associating words with degrees of fear, joy, surprise, anger, anticipation, etc. [sent-41, score-0.186]

23 Turning our attention now to automatic semantic analysis of fiction, it seems that narrative modeling and summarization has been the most intensively studied application. [sent-44, score-0.16]

24 Narrative structure has also been studied by representing character interactions as networks. [sent-46, score-0.059]

25 Mutton (2004) adapted methods for extracting social networks from Internet Relay Chat (IRC) to mine Shakespeare’s plays for their networks. [sent-47, score-0.161]

26 Extending this line of work to novels, Elson and McKeown (2010) developed a reliable method for speech attribution in unstructured texts, and then used this method to successfully extract social networks from Victorian novels (Elson et al. [sent-48, score-0.38]

27 While structure is undeniably important, we believe analyzing a narrative’s emotions is essential to capturing the ‘reading experience,’ which is a view others have held. [sent-51, score-0.146]

28 Alm and Sproat (2005) analyzed Brothers Grimm fairy tales for their ‘emotional trajectories,’ finding emotion typically increases as a story progresses. [sent-52, score-0.215]

29 Mohammad (201 1) scaled-up their work by using a crowdsourced emotion lexicon to track emotion dynam- ics over the course of many novels and plays, including Shakespeare’s. [sent-53, score-0.432]

30 In the most recent work we are aware of, Elsner (2012) analyzed emotional trajectories at the character level, showing how Miss Elizabeth Bennet’s emotions change over the course of Pride and Prejudice. [sent-54, score-0.383]

31 3 Character-to-Character Sentiment Analysis Figure 1: The characters in Hamlet are ranked by Hamet’s sentiment towards them. [sent-55, score-0.457]

32 We attempt to further Elsner’s line of work by leveraging text structure (as Mutton and Elson did) and knowlege-based SA to track the emotional trajectories of interpersonal relationships rather than of a whole text or an isolated character. [sent-57, score-0.356]

33 This assumption doesn’t always hold; it is not uncommon to find a scene in which two characters are expressing feelings about someone offstage. [sent-59, score-0.33]

34 Yet our initial results on Shakespeare’s plays show that the instances of face-to-face dialogue produce a strong enough signal to generate sentiment rankings that match our expectations. [sent-60, score-0.502]

35 For example, Hamlet’s sentiment rankings upon the conclusion of his play are shown in Figure 1. [sent-61, score-0.448]

36 Not surprisingly, Claudius draws the most negative sentiment from Hamlet, receiving a score of -27. [sent-62, score-0.422]

37 On the other hand, Gertrude is very well liked by Hamlet (+24), which is unexpected (at least to 480 us) since Hamlet suspects that his mother was involved in murdering King Hamlet. [sent-63, score-0.073]

38 Figure 2: The above chart tracks how Gertrude’s and Hamlet’s sentiment towards one another changes over the course of the play. [sent-64, score-0.442]

39 Hamlet’s sen- timent for Gertrude is denoted by the black line, and Gertrude’s for Hamlet is marked by the opposite boundary of the dark/light gray area. [sent-65, score-0.277]

40 1 Peering into the Queen’s Closet To gain more insight into this mother-son relationship, we examined how their feelings towards one another change over the course of the play. [sent-68, score-0.2]

41 Figure 2 shows the results of dynamic characterto-character sentiment analysis on Gertrude and Hamlet. [sent-69, score-0.389]

42 The running total of Hamlet’s sentiment valence toward Gertrude is tracked by the black line, and Gertrude’s feelings toward her son are tracked by the opposite boundary of the light/dark gray area. [sent-70, score-1.027]

43 The line graph shows a dramatic swing in sentiment around line 2,250, which corresponds to Act iii, Scene iv. [sent-71, score-0.474]

44 In this scene, entitled The Queen ’s Closet, Hamlet confronts his mother about her involvement in King Hamlet’s death. [sent-72, score-0.11]

45 Gertrude is shocked at the accusation, revealing she never suspected Ham- let’s father was murdered. [sent-73, score-0.03]

46 King Hamlet’s ghost even points this out to his son: “But, look, amazement on thy mother sits” (3. [sent-74, score-0.118]

47 Hamlet then comes to the realization that his mother had no involvement in the murder and probably married Claudius more so to preserve stability in the state. [sent-77, score-0.152]

48 As a result, Hamlet’s affection towards his mother grows, as exhibited in the sentiment jump from -1 to 22. [sent-78, score-0.499]

49 But this scene has the opposite affect on Gertrude: she sees her son murder an innocent man (Polonius) and talk to an invisible presence (she cannot see King Hamlet’s ghost). [sent-79, score-0.27]

50 Because of Gertrude’s realization, it is only natural that her sentiment undergoes a sharply negative change (1to -19). [sent-81, score-0.394]

51 2 Analyzing Shakespeare’s Most Famous Couples Figure 3: Othello’s sentiment for Desdemona is denoted by the black line, and Desdemona’s for Othello is marked by the opposite boundary of the dark/light gray area. [sent-83, score-0.637]

52 As expected, the line graph shows Othello has very strong positive emotion towards his new wife at the beginning of the play, but this positivity quickly degrades as Othello falls deeper and deeper into Iago’s deceit. [sent-84, score-0.27]

53 After running this automatic analysis on all of Shakespeare’s plays, not all the results examined were as enlightening as the Hamlet vs. [sent-85, score-0.029]

54 We clearly see Othello’s love for his new bride climaxes in the first third of the play and then rapidly degrades due to Iago’s deceit while Desdemona’s feelings for Othello stay positive until the very end of the play when it is clear Othello’s love for her has become poisoned. [sent-91, score-0.465]

55 As expected, the two exhibit rapidly increasing positive sentiment for each other that only tapers when the play takes a tragic course in the latter half. [sent-94, score-0.522]

56 The phases of Petruchio’s courtship can be seen: first he is neutral to her, then ‘tames’ her with a 481 period of negative sentiment, and finally she embraces him, as shown by the increasingly positive sentiment exhibited in both directions. [sent-97, score-0.45]

57 Figure 4: Juliet’s sentiment for Romeo is denoted by the black line, and Romeo’s for Juliet is marked by the opposite boundary of the gray area. [sent-98, score-0.637]

58 Aligning with our expectations, both characters exhibit strong positive sentiment towards the other throughout the play. [sent-99, score-0.486]

59 Unfortunately, we do not have room in this paper to discuss further examples, but a visualization of sentiment dynamics between any pair of characters in any of Shakespeare’s plays can be seen at www. [sent-100, score-0.551]

60 Figure 5: Petruchio’s sentiment for Katharina is denoted by the black line, and Katharina’s for Petruchio is marked by the opposite boundary of the dark/light gray area. [sent-104, score-0.637]

61 The period from line 1200 to line 1700, during which Petruchio exhibits negative sentiment, marks where he is ‘taming’ the ‘shrew. [sent-105, score-0.148]

62 ’ 4 Future Work While this paper presents experiments on just Shakespeare’s plays, note that the described technique can be extended to any work of fiction writ- ten since the Elizabethan Period. [sent-106, score-0.086]

63 The sentiment lexicon we used, AFINN, is designed for modern English; thus, it should only provide better analysis on works written after Shakespeare’s. [sent-107, score-0.389]

64 Furthermore, character-to-character analysis should be able to be applied to novels (and other unstructured fiction) if Elson and McKeown’s (2010) speaker attribution technique is first run on the work. [sent-108, score-0.278]

65 Not only can these techniques be extended to novels but also be made more precise. [sent-109, score-0.138]

66 For instance, the assumption that the current speaker’s sentiment is directed toward the previous speaker is rather naive. [sent-110, score-0.436]

67 A speech could be analyzed for context clues that signal that the character speaking is not talking about someone present but about someone out of the scene. [sent-111, score-0.193]

68 The sentiment could then be redirected to the not-present character. [sent-112, score-0.36]

69 Furthermore, detecting subtle rhetorical features such as irony and deceit would markedly improve the accuracy ofthe analysis on some plays. [sent-113, score-0.074]

70 For example, our character-to-character analysis fails to detect that Iago hates Othello because Iago gives his commander constant lip service in order to manipulate him–only revealing his true feelings at the play’s conclusion. [sent-114, score-0.177]

71 5 Conclusions As demonstrated, shallow, un-customized sentiment analysis can be used in conjunction with text structure to analyze interpersonal relationships described within a play and output an interpretation that matches reader expectations. [sent-115, score-0.554]

72 This character-to-character sentiment analysis can be done statically as well as dynamically to possibly pinpoint influential moments in the narrative (which is how we noticed the importance of Hamlet’s Act 3, Scene 4 to the Hamlet-Gertrude relationship). [sent-116, score-0.565]

73 Clac and clac-nb: knowledge-based and corpus-based approaches to sentiment tagging. [sent-133, score-0.36]

74 From once upon a time to happily ever after: Tracking emotions in novels and fairy tales. [sent-184, score-0.301]

75 Inferring and visualizing social networks on internet relay chat. [sent-190, score-0.129]


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