Sentiment Analysis and Subjectivity

Bing Liu

The Web has dramatically changed the way that people express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user-generated content. This online word- of-mouth behavior represents new and measurable sources of information with many practical applications. Now if one wants to buy a product, he/she is no longer limited to asking his/her friends and families because there are many product reviews on the Web which give opinions of existing users of the product. For a company, it may no longer be necessary to conduct surveys, organize focus groups or employ external consultants in order to find consumer opinions about its products and those of its competitors because the user-generated content on the Web can already give them such information. However, finding opinion sources and monitoring them on the Web can still be a formidable task because there are a large number of diverse sources, and each source may also have a huge volume of opinionated text (text with opinions or sentiments). In many cases, opinions are hidden in long forum posts and blogs. It is difficult for a human reader to find relevant sources, extract relevant sentences with opinions, read them, summarize them, and organize them into usable forms. Thus, automated opinion discovery and summarization systems are needed. Sentiment analysis, also known as opinion mining, grows out of this need.

This chapter gives an introduction to sentiment analysis and subjectivity (or opinion mining). Due to many challenging research problems and a wide variety of practical applications, it has been a very active research area in recent years. In fact, it has spread from computer science to management science. This chapter first presents an abstract model of sentiment analysis, which formulates the problem and provides a common framework to unify different research directions. It then discusses the most widely studied topic of sentiment and subjectivity classification, which determines whether a document or sentence is opinionated, and if so whether it carries a positive or negative opinion. We then describe feature-based sentiment analysis which exploits the full power of the abstract model. This is followed an introduction to opinion retrieval and search. Finally, the topic of opinions spam and utility of opinions are also discussed.

Bibtex Citation

  @incollection{liu-handbook10,
   author = {Bing Liu},
   title = {Sentiment Analysis and Subjectivity},
   booktitle = {Handbook of Natural Language Processing, Second Edition},
   editor = {Nitin Indurkhya and Fred J. Damerau},
   publisher = {CRC Press, Taylor and Francis Group},
   address = {Boca Raton, FL},
   year = {2010},
   note = {ISBN 978-1420085921}
  }