From Once Upon a Time to Happily Ever After: Tracking Emotions in Books and Mail! Saif Mohammad! National Research Council Canada!
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1 From Once Upon a Time to Happily Ever After: Tracking Emotions in Books and Mail! Saif Mohammad! National Research Council Canada!
2 Road Map!!" Motivation and background!!" Emotion Lexicon!!" Analysis of emotion words in mail!!" Analysis of emotion words in books! Source papers:! Crowdsourcing a Word-Emotion Association Lexicon! Saif Mohammad and Peter Turney, To Appear In Computational Intelligence, Wiley Blackwell Publishing Ltd.! Tracking Sentiment in Mail: How Genders Differ on Emotional Axes! Saif Mohammad and Tony Yang, In Proceedings of the ACL 2011 Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), June 2011, Portland, OR.! From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales! Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), June 2011, Portland, OR.! Saif Mohammad. Tracking Emotions in Books and Mail.! 2!
3 Road Map!!" Motivation and background!!" Emotion Lexicon!!" Analysis of emotion words in mail!!" Analysis of emotion words in books! Source papers:! Crowdsourcing a Word-Emotion Association Lexicon! Saif Mohammad and Peter Turney, To Appear In Computational Intelligence, Wiley Blackwell Publishing Ltd.! Tracking Sentiment in Mail: How Genders Differ on Emotional Axes! Saif Mohammad and Tony Yang, In Proceedings of the ACL 2011 Workshop on ACL 2011 Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), June 2011, Portland, OR.! From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales! Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), June 2011, Portland, OR.! Saif Mohammad. Tracking Emotions in Books and Mail.! 3!
4 Road Map!!" Motivation and background!!" Emotion Lexicon!!" Analysis of emotion words in mail!!" Analysis of emotion words in books! Source papers:! Crowdsourcing a Word-Emotion Association Lexicon! Saif Mohammad and Peter Turney, To Appear In Computational Intelligence, Wiley Blackwell Publishing Ltd.! Tracking Sentiment in Mail: How Genders Differ on Emotional Axes! Saif Mohammad and Tony Yang, In Proceedings of the ACL 2011 Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), June 2011, Portland, OR.! From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales! Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), June 2011, Portland, OR.! Saif Mohammad. Tracking Emotions in Books and Mail.! 4!
5 Road Map!!" Motivation and background!!" Emotion Lexicon!!" Analysis of emotion words in mail!!" Analysis of emotion words in books! Source papers:! Crowdsourcing a Word-Emotion Association Lexicon! Saif Mohammad and Peter Turney, To Appear In Computational Intelligence, Wiley Blackwell Publishing Ltd.! Tracking Sentiment in Mail: How Genders Differ on Emotional Axes! Saif Mohammad and Tony Yang, In Proceedings of the ACL 2011 Workshop on ACL 2011 Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), June 2011, Portland, OR.! From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales! Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), June 2011, Portland, OR.! Saif Mohammad. Tracking Emotions in Books and Mail.! 5!
6 Acknowledgments! Peter Turney! National Research Council Canada! Turkers! Tony (Wenda) Yang! Simon Fraser University! Saif Mohammad. Tracking Emotions in Books and Mail.! 6!
7 Emotions! (Phil, from the San Francisco Chronicle)# speaker/writer! Death threats over South Park episode# Event! When your cartoon can get you killed listener/reader! Extremists Trey Parker, Matt Stone Participants Participants Saif Mohammad. Tracking Emotions in Books and Mail.! 7
8 Words! associated with joy! When your cartoon can get you killed associated with sadness! Saif Mohammad. Tracking Emotions in Books and Mail.! 8
9 Our goal!!" Create a large word-emotion association lexicon through input from people.!" Examples: "" vampire is typically associated with fear "" startle is associated with surprise "" bliss is associated with joy "" death is associated with sadness "" eager is associated with anticipation!" Use the lexicon to understand the use of emotion words in text. Saif Mohammad. Tracking Emotions in Books and Mail.! 9
10 Which Emotions? Saif Mohammad. Tracking Emotions in Books and Mail.!10!
11 Plutchik, 1980: Eight Basic Emotions!" Joy!!" Trust!!" Fear!!" Surprise!!" Sadness!!" Disgust!!" Anger!!" Anticipation! Saif Mohammad. Tracking Emotions in Books and Mail.!11!
12 Using Mechanical Turk for! CROWDSOURCING A! WORD-EMOTION ASSOCIATION LEXICON! Saif Mohammad. Tracking Emotions in Books and Mail.!12!
13 Crowdsourcing!" Benefits!!" Inexpensive!!" Convenient and time-saving! "" Especially for large-scale annotation!!" Challenges!!" Quality control! "" Malicious annotations! "" Inadvertent errors! Saif Mohammad. Tracking Emotions in Books and Mail.!13!
14 Target n-grams! Must be:!!" in Roget!s Thesaurus"!" high-frequency term in the Google n-gram corpus! Saif Mohammad. Tracking Emotions in Books and Mail.!14!
15 Word-Choice Question! Q1. Which word is closest in meaning to shark?#.!! car! tree! fish! olive"!" Generated automatically!!" Near-synonym taken from thesaurus!!" Distractors are randomly chosen!!" Guides Turkers to desired sense!!" Aides quality control!!" If Q1 is answered incorrectly:!!" Response to Q2 is discarded! Saif Mohammad. Tracking Emotions in Books and Mail.!15!
16 Association Questions! Q2. How much is shark associated with the emotion fear? (for example, horror and scary are strongly associated with fear)!" shark is not associated with fear!" shark is weakly associated with fear!" shark is moderately associated with fear!" shark is strongly associated with fear!" Eight such questions for the eight emotions.!" Two such questions for positive or negative.! Saif Mohammad. Tracking Emotions in Books and Mail.!16!
17 Emotion Lexicon!" Each word-sense pair is annotated by 5 Turkers!!" About 10% of the assignments were discarded due to incorrect response to Q1 (gold question)!!" Targets with less than 3 valid assignments removed!!" NRC Emotion Lexicon!!" sense-level lexicon! "" word sense pairs: 24,200!!" word-level lexicon! "" union of emotions associated with the different senses of a word! "" word types: 14,200! Saif Mohammad. Tracking Emotions in Books and Mail.!17!
18 MOTIVATION:! EMOTION ANALYSIS OF MAIL! Saif Mohammad. Tracking Emotions in Books and Mail.!18!
19 Applications! of emotion analysis of mail!!" Tracking emotions in one$s own !!" Developing a depression happiness index!!" Example: determining risk of repeat suicide attempt # (Osgood and Walker, 1959; Pestian et al., 2008)!!" Search!!" Example: locating snippets expressing anger, for customer relation management (Diaz and Ruz, 2002; Dube and Maute, 1996)!!" Analyzing Social Behaviour!!" Example: how genders communicate in the work place!!" Analyzing Social Networks!!" Example: Tracking flow of emotions in a social network!!" Developing Writing Aids!!" Example: Aids for avoiding misinterpretation (Liu et al., 2003)! Saif Mohammad. Tracking Emotions in Books and Mail.!19!
20 Sources of Mail for Research!!" Enron Corpus (Klimt and Yang, 2004)!!" Work-place communication!!" 200,000 s!!" October 1998 to June 2002!!" 150 of the senior management at Enron Corporation!!" ECUE Spam Datasets!!" Phishing Datasets! Saif Mohammad. Tracking Emotions in Books and Mail.!20!
21 Sources of Mail for Research (continued)!" Suicide Notes Corpus!!" 21 notes posted by Art Kleiner!!" I2B will release a few thousand suicide notes!!" Hate Mail Corpus!!" 279 pieces of hate mail sent to the Millenium Project"!" Love Letters Corpus!!" 348 postings at lovingyou.com! Saif Mohammad. Tracking Emotions in Books and Mail.!21!
22 Saif Mohammad. Tracking Emotions in Books and Mail.!22!
23 Saif Mohammad. Tracking Emotions in Books and Mail.!23!
24 relative salience of joy words Saif Mohammad. Tracking Emotions in Books and Mail.!24!
25 relative salience of disgust words Saif Mohammad. Tracking Emotions in Books and Mail.!25!
26 WOMEN vs. MEN: Comparative Analysis of EMOTION WORDS IN WORK-PLACE ! Saif Mohammad. Tracking Emotions in Books and Mail.!26!
27 Genders and Language!!" Huge literature at the intersection of gender and language # (see bibliographies by Schiffman, 2002 and Sunderland et al., 2002)!!" Men and women use language differently, even in # computer-mediated communication (Boneva et al., 2001)!!" Women! "" foster personal relations (Deaux and Major, 87; Eagly and Steffen, 84)! "" share concerns and support others (Boneva et al., 2001)!!" Men! "" communicate for social position (Tannen, 1991)! "" prefer to talk about activities # (Caldwell and Peplau, 1982; Davidson and Duberman, 1982)! What are the differences in how genders use emotion words in work-place ?! Saif Mohammad. Tracking Emotions in Books and Mail.!27!
28 Corpus!!" Used the Enron Corpus!!" Removed mails with less than 50 and more than 200 words# (Cheng et al., 2009)!!" Identified gender of senders by name!!" 41 female, 89 male, 20 untagged!!" Mails sent by gender-unknown employees removed!!" 32,045 mails remaining! "" 19,920 sent by men! "" 12,125 sent by women! Saif Mohammad. Tracking Emotions in Books and Mail.!28!
29 relative salience of trust words Saif Mohammad. Tracking Emotions in Books and Mail.!29!
30 relative salience of joy words Saif Mohammad. Tracking Emotions in Books and Mail.!30!
31 relative salience of anticipation words Saif Mohammad. Tracking Emotions in Books and Mail.!31!
32 relative salience of sadness words Saif Mohammad. Tracking Emotions in Books and Mail.!32!
33 relative salience of fear words
34 relative salience of joy words
35 MOTIVATION:! EMOTION ANALYSIS OF BOOKS! Saif Mohammad. Tracking Emotions in Books and Mail.!35!
36 Number of Books Published in a Year (source: Wikipedia) Saif Mohammad. Tracking Emotions in Books and Mail.!36!
37 Sources of Digitized Books!!" Project Gutenberg:!!" more than 34,000 books!!" Google Books Corpus (GBC):!!" 5.2 million books published from 1600 to 2009!!" English portion has 361 billion words!!" 1-grams, 2-grams, 3-grams, 4-grams, 5-grams! Saif Mohammad. Tracking Emotions in Books and Mail.!37!
38 Applications! of emotion analysis of books!!" Search!!" Example: Which Brothers Grimm tales are the darkest?!!" Social Analysis!!" Example: How have books portrayed entities over time? (Michel et al. 2011)!!" Literary Analysis!!" Example: Is the distribution of emotion words in fairy tales significantly different from that in novels?!!" Summarization!!" Example: Automatically generate summaries that capture different emotional states of characters in a novel!!" Analyzing Persuasion Tactics!!" Example: how emotion words are used for persuasion? (Mannix, 1992; Bales, 1997)! Saif Mohammad. Tracking Emotions in Books and Mail.!38!
39 Applications! of emotion analysis of books!!" Search!!" Example: Which Brothers Grimm tales are the darkest?!!" Social Analysis!!" Example: How have books portrayed entities over time? (Michel et al. 2011)!!" Literary Analysis!!" Example: Is the distribution of emotion words in fairy tales significantly different from that in novels?!!" Summarization!!" Example: Automatically generate summaries that capture different emotional states of characters in a novel!!" Analyzing Persuasion Tactics!!" Example: how emotion words are used for persuasion? (Mannix, 1992; Bales, 1997)! Saif Mohammad. Tracking Emotions in Books and Mail.!39!
40 Saif Mohammad. Tracking Emotions in Books and Mail.!40!
41 Saif Mohammad. Tracking Emotions in Books and Mail.!41!
42 relative salience of trust words Saif Mohammad. Tracking Emotions in Books and Mail.!42!
43 relative salience of sadness words Saif Mohammad. Tracking Emotions in Books and Mail.!43!
44 Flow of Emotions! Saif Mohammad. Tracking Emotions in Books and Mail.!44!
45 Saif Mohammad. Tracking Emotions in Books and Mail.!45!
46 Emotion Word Density average number of emotion words in every X words Brothers Grimm fairy tales ordered as per increasing negative word density. X = 10,000.! Saif Mohammad. Tracking Emotions in Books and Mail.!46!
47 Co-occurring Emotion Words!!" Examined emotion words in proximity of target entities!!" Used the Google Books Corpus!!" Looked for emotion words in 5-grams that had the target! "" Ignored emotion associated with target word!!" Grouped information into 5-year bins! Saif Mohammad. Tracking Emotions in Books and Mail.!47!
48 Percentage of fear words in close proximity to occurrences of America, China, Germany, and India in books. Saif Mohammad. Tracking Emotions in Books and Mail.!48!
49 Percentage of anger words in close proximity to occurrences of man and woman in books. Saif Mohammad. Tracking Emotions in Books and Mail.!49!
50 Summary!!" Created a large word-emotion association lexicon!!" Used simple measures and visualizations to quantify and track the use of emotion words in texts!!" Used the Enron Corpus!!" To show use of emotion words by men and women in a work-place setting!!" Used the Brothers Grimm fairy tales!!" showed texts can be ordered for affect-based search!!" Used the Google Books Corpus!!" tracked emotion associations of entities over time! Saif Mohammad. Tracking Emotions in Books and Mail.!50!
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