Opinion mining and sentiment analysis

• monograph itself:
  • published version
  • authors-formatted version: may have slight differences in wording from the final print version; has fewer pages than the published version due to tighter formatting; includes hyperref links for navigation and full first names in the bibliography.
  • Now Publishers website: html version with hyperlinks to all references for series subscribers, and order forms for book or e-book versions (use promotion code INR002001 for a 50% discount). Note also the description of the publisher's philosophy and benefits of their approach. The publishers offer special pricing if 10+ copies are required for use in a course or otherwise.
  • Amazon site
• bibliography:
  • bibtex file: original except for addition of the survey itself (336 references total)
  • search 'n sort interface
• AAAI 2008 invited talk slides, based for the most part on this monograph

Abstract:
An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, vulnerability to manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

Mentions: “Congratulations” (Matthew Hurst) | “THE survey to read”, “tremendous resource” (Jeffrey Carr) | “Excellent” (George Tziralis) | “excellent points” (Jessica Hullman)

Table of Contents:

1. Introduction
   1. The demand for information on opinions and sentiment
   2. What might be involved? An example examination of the construction of an opinion/review search engine
   3. Our charge and approach
   4. Early history
   5. A note on terminology: Opinion mining, sentiment analysis, subjectivity, and all that
2. Applications
   1. Applications to review-related websites
   2. Applications as a sub-component technology
   3. Applications in business and government intelligence
   4. Applications across different domains
3. General Challenges
   1. Contrasts with standard fact-based textual analysis
   2. Factors that make opinion mining difficult
4. Classification and Extraction
   Part One: Fundamentals
   1. Problem formulations and key concepts
      1. Sentiment polarity and degrees of positivity
      2. Subjectivity detection and opinion identification
      3. Joint topic-sentiment analysis
      4. Viewpoints and perspectives
      5. Other non-factual information in text
   2. Features
      1. Term presence vs. frequency
      2. Term-based features beyond term unigrams
      3. Parts of speech
      4. Syntax
      5. Negation
      6. Topic-oriented features
   Part Two: Approaches
   3. The impact of labeled data
   4. Domain adaptation and topic-sentiment interaction
      1. Domain considerations
      2. Topic (and sub-topic or feature) considerations
   5. Unsupervised approaches
      1. Unsupervised lexicon induction
      2. Other unsupervised approaches
   6. Classification based on relationship information
      1. Relationships between sentences and between documents
      2. Relationships between discourse participants
      3. Relationships between product features
      4. Relationships between classes
   7. Incorporating discourse structure
   8. Language models
   9. Special considerations for extraction
      1. Identifying product features and opinions in reviews
      2. Problems involving opinion holders
5. Summarization
   1. Single-document opinion-oriented summarization
   2. Multi-document opinion-oriented summarization
      1. Some problem considerations
      2. Textual summaries
      3. Non-textual summaries
      4. Review(er) quality
6. Broader Implications
   1. Economic impact of reviews
      1. Surveys summarizing relevant economic literature
      2. Economic-impact studies employing automated text analysis
      3. Interactions with word of mouth (WOM)
   2. Implications for manipulation
7. Publicly Available Resources
   1. Datasets
      1. Acquiring labels for data
      2. An annotated list of datasets
   2. Evaluation campaigns
      1. TREC opinion-related competitions
      2. NTCIR opinion-related competitions
   3. Lexical resources
   4. Tutorials, bibliographies, and other references
8. Concluding Remarks
9. References

Lillian Lee's home page | Lillian Lee's co-authored papers on sentiment analysis