Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, appraisals, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. Many related names and slightly different tasks – for example, sentiment analysis, opinion mining, opinion analysis, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, and review mining – are now all under the umbrella of sentiment analysis. The term sentiment analysis perhaps first appeared in Nasukawa and Yi (2003), and the term opinion mining first appeared in Dave et al. (2003). However, research on sentiment and opinion began earlier (Wiebe, 2000; Das and Chen, 2001; Tong, 2001; Morinaga et al., 2002; Pang et al., 2002; Turney, 2002). Even earlier related work includes interpretation of metaphors; extraction of sentiment adjectives; affective computing; and subjectivity analysis, viewpoints, and affects (Wiebe, 1990, 1994; Hearst, 1992; Hatzivassiloglou and McKeown, 1997; Picard, 1997; Wiebe et al., 1999). An early patent on text classification included sentiment, appropriateness, humor, and many other concepts as possible class labels (Elkan, 2001).