Sentiment Analysis

(thanks to Matt Baker)

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Laptop Purchase

How will you decide?

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Survey Says

81% internet users

• online product research 1+ times

20% internet users

• online product research daily

73-87% consumers of restaurant, hotel, service reviews

• reviews significantly impact purchasing decisions

comScore/the Kelsey group, “Online consumer-generated reviews have significant impact on offline purchase behavior,” Press Release, http://www.comscore.com/press/release.asp?press=1928, November 2007. Quoted in Pang and Lee, 2008, “Opinion Mining and Sentiment Analysis”

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Survey Says

20-99% consumers

• willing to pay more for 5- than 4-star-rated product

32% consumers

• rated online product, service, person

30% consumers

• posted online comment or review

J. A. Horrigan, “Online shopping,” Pew Internet & American Life Project Report, 2008. Quoted in Pang and Lee, 2008, “Opinion Mining and Sentiment Analysis”

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Definition

Many scholars view sentiment analysis as “the computational treatment of opinion,

sentiment, and subjectivity in text”*

How do each of these terms differ?

*Pang 2008, pg. 8

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Review of Literature

• Uses machine learning to assess polarity (positive, negative, neutral) in movie reviews – Pang, Lee, & Vaithyanathan (2002)

• Evaluates sentiment based on parts of speech (adjectives and adverbs) – Turney (2002)

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Review of Literature

• Separates objective from subjective statements and assess polarity of opinion sentences – Yu & Hatzivassiloglou (2003)

• Identifies valence shifters in text that can give information regarding the writer’s sentiment – Polanyi & Zaenen (2004)

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Review of Literature

• Expands sentiment analysis to include rankings on a scale – Pang & Lee (2005)

• Selects features from text and performs sentiment analysis on a feature level – Durant & Smith (2007)

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Twitter

How would you extract sentiment from Tweets?

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Considerations • Parts of speech • Objective statements • Subjective statements • Binary classification • Ranking • Features • Overall sentiment • Domain • Word position

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Considerations • Valence shifters*

– Words – Negation – Intensifiers – Modal operators – Irony

• Pronoun resolution • Topic relevance • Unigrams, bigrams, etc. • Syntax • Strength of polarity

*Polanyi & Zaenen (2004)

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Twitter Literature

• Sentiment word frequencies* • Emoticons *** • Unigrams*** • Bigrams*** • Parts of Speech***

*O’Conner et al. 2010 ***Go 2009

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Sample reviews (negative polarity) • a peculiar misfire that even tunney can't save . • watching queen of the damned is like reading a

research paper , with special effects tossed in . • i can't remember the last time i saw worse stunt

editing or cheaper action movie production values than in extreme ops .

• too much of nemesis has a tired , talky feel . • i felt trapped and with no obvious escape for the

entire 100 minutes . • a baffling mixed platter of gritty realism and

magic realism with a hard-to-swallow premise . • an affable but undernourished romantic comedy

that fails to match the freshness of the actress-producer and writer's previous collaboration , miss congeniality

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Sample reviews (positive polarity) • emerges as something rare , an issue movie that's so honest

and keenly observed that it doesn't feel like one . • the film provides some great insight into the neurotic

mindset of all comics -- even those who have reached the absolute top of the game .

• offers that rare combination of entertainment and education .

• perhaps no picture ever made has more literally showed that the road to hell is paved with good intentions .

• offers a breath of the fresh air of true sophistication . • a thoughtful , provocative , insistently humanizing film . • not for everyone , but for those with whom it will connect

, it's a nice departure from standard moviegoing fare . • is it a total success ? no . is it something any true film

addict will want to check out ? you bet . • engrossing and affecting , if ultimately not quite

satisfying .

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Lexical characterizations

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Tools: SentiWordNet

http://sentiwordnet.isti.cnr.it/

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Tools: Opinon Lexicon

http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon

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Online data / demos / tools

• Movie review data • NLTK

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Systems

• LingPipe: tutorial and system you can install

• Weka: blog and instructions • R: blog and pointer to code

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Competitions

Kaggle.com

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Methods: Linear Regression

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Example

Twitter with R*

*https://github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107/blob/master/R/0_start.R *http://www.inside-r.org/howto/mining-twitter-airline-consumer-sentiment

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Other applications

• Classifying speeches as for or against issues* • Discovering the political leanings of texts** • Sensing user annoyance by computers to

change interaction methods*** • Monitoring violent and hateful

propaganda**** • Tracking the world’s mood***** • Scanning emails

*Thomas, et al. 2006 **Pang 2008 ***Liscombe et al. 2005 ****Abassi 2007

References • A. Abbasi, “Affect intensity analysis of dark web forums,” in Proceedings of Intelligence and Security

Informatics (ISI), pp. 282–288, 2007. • Bo, Pang, and Lillian Lee. "Opinion Mining and Sentiment Analysis." Foundations & Trends in Information

Retrieval 2, no. 1/2 (2008): 1-135. • Bo, Pang, Lillian Lee, and Shivakumar Vaithyanathan. “Thumbs up? Sentiment Classification using Machine

Learning Techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing. (2002).

• Durant, Kathleen and Michael Smith. “Predicting the Political Sentiment of Web Log Posts using Supervised Machine Learning Techniques Coupled with Feature Selection.” 2007.

• Go, Alec. “Twitter Sentiment Analysis.” 2009. • J. Liscombe, G. Riccardi, and D. Hakkani-T¨ur, “Using context to improve emotion detection in spoken dialog

systems,” in Interspeech, pp. 1845–1848, 2005. • M. Thomas, B. Pang, and L. Lee, “Get out the vote: Determining support or opposition from congressional

floor-debate transcripts,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 327–335, 2006.

• O’Connor, Brandon, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. “From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series.” 2010.

• Pang, Bo and Lillian Lee. “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.” 2005.

• Polanyi, Livia and Annie Zaenen. “Contextual valence shifters.” Computing attitude and affect in text: Theory and applications. 2006.

• Turney, Peter. “Thumbs up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews.” Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. (2002).

• Yu, Hong and Vasileios Hatzivassiloglou. “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences.” Proceedings of the 2003 conference on Empirical methods in natural language processing. 2003.