SciSurf: Index of 'Character-to-Character Sentiment Analysis in Shakespeare's Plays'

Character-to-Character Sentiment Analysis in Shakespeare's Plays

Nalisnick, Eric T. and Baird, Henry S.

We present an automatic method for analyzing sentiment dynamics between characters in plays.

Insightful analysis of literary fiction often challenges trained human readers let alone machines.

Appears in 7 sentences as: Sentiment Analysis (2) Sentiment analysis (2) sentiment analysis (3)

In Character-to-Character Sentiment Analysis in Shakespeare's Plays

Sentiment analysis (Pang and Lee, 2008) has been successfully applied to mine social media data for emotional responses to events, public figures, and consumer products just by using emotion lexicons—lists that map words to polarity values (+1 for positive sentiment, -l for negative) or valence values that try to capture degrees of polarity.
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plays.1 2 Sentiment Analysis and Related Work
Page 1, “Introduction”
Sentiment analysis (SA) is now widely used commercially to infer user opinions from product reviews and social-media messages (Pang and Lee,
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3 Character-to-Character Sentiment Analysis
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Figure 2 shows the results of dynamic character-to-character sentiment analysis on Gertrude and Hamlet.
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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.
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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).
Page 4, “Introduction”

See all papers in Proc. ACL 2013 that mention sentiment analysis.

See all papers in Proc. ACL that mention sentiment analysis.

Appears in 4 sentences as: sentiment lexicon (2) sentiment lexicons (2)

In Character-to-Character Sentiment Analysis in Shakespeare's Plays

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
Page 1, “Introduction”
These methods are driven by sentiment lexicons , fixed lists associating words with “valences” (signed integers representing positive and negative feelings) (Kim and Hovy, 2004).
Page 2, “Introduction”
To extract these relationships, we mined for character-to-character sentiment by summing the valence values (provided by the AFINN sentiment lexicon (Nielsen, 2011)) over each instance of continuous speech and then assumed that sentiment was directed towards the character that spoke immediately before the current speaker.
Page 2, “Introduction”
The sentiment lexicon we used, AFINN, is designed for modern English; thus, it should only provide better analysis on works written after Shakespeare’s.
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See all papers in Proc. ACL 2013 that mention sentiment lexicon.

See all papers in Proc. ACL that mention sentiment lexicon.