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14 - Conclusions

Published online by Cambridge University Press:  05 June 2015

Bing Liu
Affiliation:
University of Illinois, Urbana-Champaign
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Summary

This book introduced the field of sentiment analysis or opinion mining. It presented some basic knowledge and mature techniques in detail and surveyed numerous other state-of-the-art algorithms. Owing to numerous challenging research problems and a wide variety of practical applications, sentiment analysis has been a very active research area in several computer science fields, for example, NLP, data mining, web mining, and information retrieval. It has also spread to management science (Hu et al., 2006; Archak et al., 2007; Das and Chen, 2007; Dellarocas et al., 2007; Ghose et al., 2007; Park et al., 2007; Chen and Xie, 2008) and other social science fields such as communications and political science because of its importance to business and society as a whole. With the rapid expansion of social media on the web, the importance of sentiment analysis is also growing by the day.

In the book, I first defined the problem of sentiment analysis (Chapter 2), which provides a common framework to unify different research directions and research problems in the area. It also presents a schema to convert unstructured free text to structured data, which facilitates qualitative and quantitative analysis of opinions. I then discussed the widely studied problem of document-level sentiment classification (Chapter 3), which aims to determine whether an opinion document (e.g., a product review) expresses a positive or a negative sentiment. This was followed by Chapter 4 on sentence-level subjectivity and sentiment classification. These tasks determine whether a sentence is opinionated, and if opinionated, whether it carries a positive or negative opinion. Chapters 5 and 6 focused on aspect-based sentiment analysis, which employs the full problem definition in Chapter 2. They also showed that sentiment analysis is a multifaceted problem with multiple challenging subproblems. The existing techniques for dealing with these problems were also presented. Due to a large amount of information, this topic was covered in two chapters. Chapter 5 was dedicated to the task of aspect-based sentiment classification, and Chapter 6 was devoted to the task of opinion target extraction, which includes the extraction of entities and their aspects.

Type
Chapter
Information
Sentiment Analysis
Mining Opinions, Sentiments, and Emotions
, pp. 309 - 314
Publisher: Cambridge University Press
Print publication year: 2015

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  • Conclusions
  • Bing Liu, University of Illinois, Urbana-Champaign
  • Book: Sentiment Analysis
  • Online publication: 05 June 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781139084789.015
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  • Conclusions
  • Bing Liu, University of Illinois, Urbana-Champaign
  • Book: Sentiment Analysis
  • Online publication: 05 June 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781139084789.015
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Conclusions
  • Bing Liu, University of Illinois, Urbana-Champaign
  • Book: Sentiment Analysis
  • Online publication: 05 June 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781139084789.015
Available formats
×