Sentiment analysis
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Sentiment analysis or opinion mining refers to a broad (definitionally challenged) area of natural language processing, computational linguistics and text mining. Generally speaking, it aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be their judgment or evaluation (see appraisal theory), their affective state (that is to say, the emotional state of the author when writing) or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).
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[edit] Subtasks
The basic task in sentiment analysis is classifying the polarity of a given text — whether the expressed opinion is positive or negative. Early work in that area includes [1] and [2] who applied different methods for detecting the polarity of product reviews and movie reviews respectively.
A related task is classifying a document's polarity on a multi-way scale. This was attempted by [3] and [4] (among others): [3] expanded the basic task of classifying a movie review as either positive or negative to predicting star ratings on either a 3 or a 4 star scale, while [4] performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).
Another research direction is subjectivity/objectivity identification. This task is commonly [5] defined as classifying a given text into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification [6]: the subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by [7], results are largely dependent on the definition of subjectivity used when annotating texts. However, [8] showed that removing objective sentences from a document before classifying its polarity helped improve performance.
[edit] Methods
Computers can perform automated sentiment analysis of digital texts, using elements from machine learning such as latent semantic analysis, support vector machines, "bag of words" and Semantic Orientation — Pointwise Mutual Information (See Peter Turney's [1] work in this area).
There are two main approaches, statistical and linguistic. Statistical rely heavily on mathematical and statistical comparison of the occurrences and number of negative or positive statements in the text, whereas linguistic approach tries to build a set of rules and compare the analysed text with them.
[edit] See also
[edit] External links
- Seth Grimes (Feb 19, 2008), Sentiment Analysis: Opportunities and Challenges
- Seth Grimes (Jan 22, 2008), Sentiment Analysis: A Focus on Applications
- Sentiment Analysis and Opinon Mining in Spanish (Mar 13, 2009), Sentiment Analysis: definition and applications
- Open source sentiment analysis web application, sentimenthub.com
- David Tebbutt, (July 2006). "Search moves up a notch with emotional feedback". Information World Review. http://www.iwr.co.uk/information-world-review/comment/2159993/search-moves-notch-emotional.
[edit] References
- ^ a b Peter Turney (2002). "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews". Proceedings of the Association for Computational Linguistics (ACL): 417-424.
- ^ Bo Pang; Lillian Lee and Shivakumar Vaithyanathan (2002). "Thumbs up? Sentiment Classification using Machine Learning Techniques". Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP): 79-86.
- ^ a b Bo Pang; Lillian Lee (2005). "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales". Proceedings of the Association for Computational Linguistics (ACL): 115-124.
- ^ a b Benjamin Snyder; Regina Barzilay (2007). "Multiple Aspect Ranking using the Good Grief Algorithm". Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL): 300-307.
- ^ Pang, Bo; Lee, Lillian (2008). "4.1.2 Subjectivity Detection and Opinion Identification". Opinion Mining and Sentiment Analysis. Now Publishers Inc. http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html.
- ^ Rada Mihalcea; Carmen Banea and Janyce Wiebe (2007). "Learning Multilingual Subjective Language via Cross-Lingual Projections". Proceedings of the Association for Computational Linguistics (ACL): 976-983.
- ^ Fangzhong Su; Katja Markert (2008). "From Words to Senses: a Case Study in Subjectivity Recognition". Proceedings of Coling 2008, Manchester, UK.
- ^ Bo Pang; Lillian Lee (2004). "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts". Proceedings of the Association for Computational Linguistics (ACL): 271-278.
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