Fuzzy logic applied to opinion mining: A review

Abstract The advent of Web 2.0 and its continuous growth has yielded enormous amounts of freely available user-generated information. Within this information, it is easy to find subjective texts, especially on social networks and eCommerce platforms that contain valuable information about users. Consequently, the field of opinion mining has attracted considerable interest over the last decade. Many new research articles are published every day, in which different artificial intelligence techniques (e.g., neural networks, fuzzy logic, clustering algorithms, and evolving computing) are applied to various tasks and applications related to opinion mining. Given this context, this survey presents a rigorous review of the different applications of fuzzy logic in opinion mining. The review portrays different uses of fuzzy logic and summarizes over one hundred and twenty articles published in the past decade regarding tasks and applications of opinion mining. This study is organized around three primary tasks, feature processing, review classification and emotions and also pays special attention to sentiment analysis applications whose core technique uses fuzzy logic to achieve the stated goals.