A comprehensive survey on sentiment analysis: Approaches, challenges and trends

• Published in: Knowledge-based systems Vol. 226; p. 107134
• Main Authors: Birjali, Marouane, Kasri, Mohammed, Beni-Hssane, Abderrahim
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 17.08.2021; Elsevier Science Ltd
• Subjects: Data mining; Decision analysis; Deep learning; Lexicon-based; Machine learning; Opinion mining; Sentiment analysis; Sentiment classification; Social networks; Trends; Websites; Deep learning; Sentiment analysis; Opinion mining; Lexicon-based; Machine learning; Sentiment classification
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.107134

Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and analyzing people’s opinions, sentiments, attitudes, perceptions, etc., toward different entities such as topics, products, and services. The fast evolution of Internet-based applications like websites, social networks, and blogs, leads people to generate enormous heaps of opinions and reviews about products, services, and day-to-day activities. Sentiment analysis poses as a powerful tool for businesses, governments, and researchers to extract and analyze public mood and views, gain business insight, and make better decisions. This paper presents a complete study of sentiment analysis approaches, challenges, and trends, to give researchers a global survey on sentiment analysis and its related fields. The paper presents the applications of sentiment analysis and describes the generic process of this task. Then, it reviews, compares, and investigates the used approaches to have an exhaustive view of their advantages and drawbacks. The challenges of sentiment analysis are discussed next to clarify future directions. •Sentiment analysis is constantly evolving through approaches, data and models.•The paper provides an unprecedented and comprehensive survey on sentiment analysis.•Traditional and recent models are discussed, compared and classified.•Pointing out the reasons to select the proper model for sentiment analysis.•The paper summarizes the sentiment analysis models to monitor future trends.