people often rely on their peers’ past experiences. Until recently, the main sources of information were friends and specialized magazine or websites. Now, the “social web” provides new tools to effciently create and share ideas with everyone connected to the World Wide Web. Forums, blogs, social networks, and content- sharing services help people share useful information. This information is unstructured, however, and because it’s produced for human consumption, it’s not something that’s “machine processable.” Capturing public opinion about social events, political movements, company strategies, marketing campaigns, and product preferences is garnering increasing interest from the scientifc community (for the exciting open challenges), and from the business world (for the remarkable marketing fallouts and for possible fnancial market prediction). The resulting emerging felds are opinion mining and sentiment analysis. Although commonly used interchangeably to denote the same feld of study, opinion mining and sentiment analysis actually focus on polarity detection and emotion recognition, respectively. Because the identification of sentiment is often exploited for detecting polarity, however, the two felds are usually combined under the same umbrella or even used as synonyms. Both felds use data mining and natural language processing (NLP) techniques to discover, retrieve, and distill information and opinions from the World Wide Web’s vast textual information