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Opinion Analysis for Online Reviews

by Lin Yuming

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Methods for analyzing online-review sentiment

$74.98 USD
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AlbakiReads Editorial

Methods for analyzing online-review sentiment

Lin Yuming’s introduction maps several technical layers of opinion analysis: research on opinion mining, theories, algorithms, and datasets, followed by methods for classifying sentiment. The book highlights a feature-presentation method and outlines a three-phase classification framework that automatically selects classifiers and combines their predictions through ensemble learning. It also addresses the challenge of too many possible classifier combinations, presenting a greedy algorithm for selecting base classifiers. With its emphasis on frameworks and computational methods, the volume offers a focused account of how online-review sentiment analysis can be organized and implemented.

Good fit for readers who enjoy

  • Readers seeking an introduction to computational opinion mining and sentiment classification.
  • Students or practitioners interested in classifier selection, ensemble learning, and algorithmic approaches to review analysis.

Themes

Opinion miningSentiment classificationEnsemble learningClassifier selection

Based on publisher information and book metadata.

Book overview

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.

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