Linguist 287 / CS 424P: Extracting Social Meaning and Sentiment
Meetings Tues 2:15 - 4:30 pm, Rm 460-126
Course email
Instructor Dan Jurafsky Chris Potts Gayle McElvain (TA)
Office hours T 11:00-12:00 W 2:00-3:00
Th 10:30-11:30
Th 11:30-12:30
Office 460-117 460-101 460-40B
Sep 21 Overview of topics; Sentiment lexicons (Chris)
(no paragraphs for this batch)
  1. Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2200-2204. European Language Resources Association (ELRA).
  2. Hatzivassiloglou, Vasileios and Kathleen R. McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, 174-181.
  3. Velikovich, Leonid, Sasha Blair-Goldensohn, Kerry Hannan, and Ryan McDonald. 2010. The viability of web-derived polarity lexicons. Proceedings of NAACL 2010.
Chris's handout on sentiment lexicons
Data homework 1 (due before class on Sep 28)
Sep 28 Sentiment classification (Chris)
(paragraphs due by 9:00 pm on Sep 27)
  1. Pang, Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1): 1–135. Chapter 4.
  2. Pang, Bo, Lee, Lillian, and Vaithyanathan, Shivakumar. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 79-86. ACL.
  3. Kennedy, Alistair and Diana Inkpen. 2006. Sentiment classification of movie reviews using
    contextual valence shifters
    . Computational Intelligence 22:110-125.
Chris's handout on sentiment classification
Data homework 2 (due before class on Oct 5)
Detecting imposers
  1. Nitin Jindal and Bing Liu. 2008. Opinion spam and analysis. Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM-2008). [Links to associated data]
Oct 5Sentiment summarization (Chris)
(paragraphs due by 9:00 pm on Oct 4)
  1. Pang, Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1): 1–135. Chapters 5.
  2. Blair-Goldensohn, Sasha, Kerry Hannan, Ryan McDonald, Tyler Neylon, George A. Reis, and Jeff Reynar. 2008. Building a sentiment summarizer for local service reviews. In WWW Workshop on NLP in the Information Explosion Era (NLPIX).
Chris's slides on visual sentiment summarization; handout on textual sentiment summarization
Data Homework 2 due before class today. Data Homework 3 is to be turned in in parts; first part is due before class on Oct 12.
Reader responses
  1. Danescu-Niculescu-Mizil, Cristian, Gueorgi Kossinets, Jon Kleinberg, Lillian Lee. 2009. How opinions are received by online communities: A case study on helpfulness votes. Proceedings of WWW, 141-150.
  2. Ghose, Anindya and Panagiotis G. Ipeirotis. 2007. Designing novel review ranking systems: Predicting the usefulness and impact of reviews. Proceedings of ICEC 2007.
Oct 12 Prosody and speech fundamentals (Dan)
(No paragraphs for this batch: instead do first part of homework below)
  1. Jurafsky and Martin. 2009. Speech and Language Processing, Chapter 7, Phonetics.
  2. Jurafsky and Martin. 2009. Speech and Language Processing, Chapter 8, Speech Synthesis, pages 262-271
  3. Tepperman, Joseph, David Traum, and Shrikanth Narayanan. 2006. Yeah right: Sarcasm recognition for spoken dialogue systems. ICSLP 2006.
  4. Praat:
Dan's slides on prosody
Prosody homework in three parts; part I is due today, parts II and III are due Oct 15 by 2:15pm
Additional PRAAT resources:
  1. »
  2. »
  3. » http://www.linguistics.ucla.e du/faciliti/facilities/acoustic/praat.html
Oct 19 Flirtation and Personality (Dan)
(paragraphs due by 9:00 pm on Oct 18)
  1. Rajesh Ranganath, Dan Jurafsky, and Dan McFarland. 2009. It's Not You, it's Me: Detecting Flirting and its Misperception in Speed-Dates. Proceedings of EMNLP 2009.
  2. Dan Jurafsky, Rajesh Ranganath, and Dan McFarland. 2009. Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. Proceedings of NAACL HLT 2009.
  3. F. Mairesse, M. Walker, M. Mehl, and R. Moore. 2007. Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research (JAIR), 30:457-500.
Dan's slides on flirtation, dating, personality
Literature Review due before class today. Please also include in your lit review one sentence describing your final project idea and how the lit review relates to it.
  1. OkTrends weblog post: The big lies people tell in online dating
  2. Liscombe, Jackson, Julia Hirschberg, and Jennifer J. Venditti. 2005. Detecting certainness in spoken tutorial dialogues. In Proceedings of Interspeech (Eurospeech). Lisbon, Portugal.
Oct 26 Emotion (Dan and guest lecturer Stefan Steidl)
(paragraphs due by 9:00 pm on Oct 25)
  1. Ekman, Paul. 1993. Facial Expressions Of Emotion. American Psychologist 48:384-392.
  2. Braun, Angelika and Matthias Katerbow. 2005. Emotions in dubbed speech: An intercultural approach with respect to F0. Interspeech 2005.
  3. J. Ang, R. Dhillon, A. Krupski, E. Shriberg, and A. Stolcke. 2002. Prosody-Based Automatic Detection of Annoyance and Frustration in Human-Computer Dialog. In INTERSPEECH-02.
  4. Julie Robson and Janet MackenzieBeck. 1999. Hearing smiles - perceptual, acoustic and production aspects of labial spreading, ICPhS-99, San Francisco, 219-222.
Dan's slides on emotion [pptx] [Dan's pdf]    Stefan's slides
Project proposal due before class today
  1. Laukka, P., Neiberg, D., Forsell, M., Karlsson, I., & Elenius, K. 2011. Expression of Affect in Spontaneous Speech: Acoustic Correlates and Automatic Detection of Irritation and Resignation. Computer Speech & Language 25(1): 84-104
Nov 2 Deception (Chris)
(no paragraphs for this batch)
  1. David F. Larcker and Anastasia A. Zakolyukina. submitted manuscript. Detecting Deceptive Discussions in Conference Calls.
  2. Catalina L. Toma & Jeffrey T. Hancock. 2010. Reading between the Lines: Linguistic Cues to Deception in Online Dating Profiles. CSCW 2010.
  3. Enos, Frank, Elizabeth Shriberg, Martin Graciarena, Julia Hirschberg, and Andreas Stolcke. 2007. Detecting deception using critical segments. In Proceedings Interspeech, 1621-1624. Antwerp.
Chris's slides on deceptive language  David and Anastasia's slides on their paper
  1. M. L. Newman, J. W. Pennebaker, D. S. Berry, and J. M. Richards. 2003. Lying words: Predicting deception from linguistic style. Personality and Social Psychology Bulletin, 29:665–675.
  2. Talbot, Margaret. 2007. Duped. Can brain scans uncover lies? The New Yorker, July 2, 2007.
Nov 9 Medical applications: Depression, trauma, intoxication (Dan)
(no paragraphs for this batch)
  1. Campbell, Sherlock R. and Pennebaker, James W. 2003. The secret life of pronouns: flexibility in writing style and physical health. Psychological Science 14(1): 60–65.
  2. Nairan Ramirez-Esparza, Cindy K. Chung, Ewa Kacewicz, and James W. Pennebaker. 2008. The Psychology of Word Use in Depression Forums in English and in Spanish: Testing Two Text Analytic Approaches. Int'l AAAI Conference on Weblogs and Social Media (ICWSM) 2008.
  3. Hollien, H., DeJong, G., Martin, C. A., Schwartz, R. and Liljegren, K. Effects of ethanol intoxication on speech suprasegmentals. Journal of the Acoustical Society of America 110, 3198 - 3206. Brief note in Nature.
Dan's slides [pptx]
More intoxicated?
  1. S. S. Rude, E. M. Gortner, and J. W. Pennebaker. 2004. Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 18:1121-1133.
  2. Johnson, Keith, David B. Pisoni. and Robert H. Bernacki. 1990. Do voice recordings reveal whether a person is intoxicated?: A case study. Phonetica. 47: 215-237.
  3. Levit, Michael, Richard Huber, Anton Batliner, and Elmar Noeth. 2001. Use of prosodic speech characteristics for automated detection of alcohol intoxication. ISCA ITRW on Prosody in Speech Recognition and Understanding.
Nov 16 Political science (Chris)
(no paragraphs for this batch)
  1. Burt L. Monroe, Michael P. Colaresi, and Kevin M. Quinn. 2008. Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict. Political Analysis (2008) 16:372–403.
  2. Thomas, Matt, Pang, Bo, and Lee, Lillian. 2006. Get out the vote: determining support or opposition from Congressional floor-debate transcripts. In Proceedings of EMNLP 2006, 327–335.
  3. Tae Yano, Philip Resnik, and Noah A. Smith. 2010. Shedding (a Thousand Points of) Light on Biased Language. In Proceedings of the NAACL-HLT Workshop on Creating Speech and Language Data With Mechanical Turk, Los Angeles, CA, June 2010.
Chris's slides on political language (and Twitter prognostication)
Project milestone due before class today
Predicting the future with Twitter
  1. Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In Proceedings of the International AAAI Conference on Weblogs and Social Media, Washington, DC, May 2010.
  2. Johan Bollen, Huina Mao, and Xiao-Jun Zeng. 2010. Twitter mood predicts the stock market. arXiv:1010.3003v1.
  3. Sitaram Asur and Bernardo A. Huberman. 2010. Predicting the future with social media. arXiv:1003.5699v1.
Nov 23 Thanksgiving break
Nov 30 General assessment: What have we learned about sentiment? (Dan and Chris)
Final slides
Dec 9 Student presentations