This article discusses the various novel ways of classifying fuzzy logic-based sentiment analysis research articles, which have not been accomplished by any other review article till date.
Authors
Srishti Vashishtha, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India.
Vedika Gupta, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Mamta Mittal, Delhi Skill and Entrepreneurship University, New Delhi, India.
Summary
Understanding and comprehending humans’ views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e-commerce platforms has led to an abundance of the real-time and free forms of opinions floating on social media platforms.
This real-world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text-based SA, audio-based SA, and fusion of text-audio features-based SA.
This article discusses the various novel ways of classifying fuzzy logic-based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance.
Finally, the article outlines a taxonomy for sentiment classification based on the technique-supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy-based SA approaches into five different classes vis-a-vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy-rule based SA, (d) Neuro-fuzzy network-based SA, and (e) Fuzzy Emotion Recognition.
Published in: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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