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:IntroductionRelated WorksPreliminariesTerm's Sentiment-Based Review Opinion AnalysisMultiple Classifier System for Opinion AnalysisOptimization of Base Classifier SelectionOpinion Spam DetectionConclusions Readership: Researchers, academics, professionals and graduate students in databases, artificial intelligence and pattern recognition.