Opinion Mining And Sentiment Analysis

Opinion Mining And Sentiment Analysis

by Bo Pang


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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. Opinion Mining and Sentiment Analysis covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. The 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. The survey includes an enumeration of the various applications, a look at general challenges and discusses categorization, extraction and summarization. Finally, it moves beyond just the technical issues, devoting significant attention to the broader implications that the development of opinion-oriented information-access services have: questions of privacy, vulnerability to manipulation, and whether or not reviews can have measurable economic impact. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. Opinion Mining and Sentiment Analysis is the first such comprehensive survey of this vibrant and important research area and will be of interest to anyone with an interest in opinion-oriented information-seeking systems.

Product Details

ISBN-13: 9781601981509
Publisher: Now Publishers
Publication date: 07/16/2008
Series: Foundations and Trends(r) in Information Retrieval , #5
Pages: 148
Product dimensions: 6.14(w) x 9.21(h) x 0.32(d)

Table of Contents

1: Introduction 2: Applications 3: General Challenges 4: Classification and Extraction 5: Summarization 6: Broader Implications 7: Publicly Available Resources 8: Concluding Remarks. References

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