Opinion Analysis for Online Reviews

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Opinion Analysis for Online Reviews by Yuming Lin, Xiaoling Wang, Aoying Zhou, World Scientific Publishing Company
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Author: Yuming Lin, Xiaoling Wang, Aoying Zhou ISBN: 9789813100466
Publisher: World Scientific Publishing Company Publication: June 2, 2016
Imprint: WSPC Language: English
Author: Yuming Lin, Xiaoling Wang, Aoying Zhou
ISBN: 9789813100466
Publisher: World Scientific Publishing Company
Publication: June 2, 2016
Imprint: WSPC
Language: English

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.

Contents:

  • Introduction
  • Related Works
  • Preliminaries
  • Term's Sentiment-Based Review Opinion Analysis
  • Multiple Classifier System for Opinion Analysis
  • Optimization of Base Classifier Selection
  • Opinion Spam Detection
  • Conclusions

Readership: Researchers, academics, professionals and graduate students in databases, artificial intelligence and pattern recognition.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.

Contents:

Readership: Researchers, academics, professionals and graduate students in databases, artificial intelligence and pattern recognition.

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