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 analysis of subjectivity, 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). Since existing research and applications of sentiment analysis have focused primarily on written text, it has been an active research field of natural language processing (NLP). However, the topic has also been widely studied in data mining, web mining, and information retrieval because many researchers in these fields deal with text data. My own first paper (Hu and Liu, 2004) on the topic was published in the proceedings of the data mining conference KDD (SIGKDD International Conference on Knowledge Discovery and Data Mining) in 2004. This paper defined the aspect-based sentiment analysis and summarization framework and some basic ideas and algorithms for solving the problem that are commonly used in research and industrial systems today.

Not surprisingly, there has been some confusion among practitioners and even researchers about the difference between sentiment and opinion and whether the field should be called sentiment analysis or opinion mining.