Sentiment Analysis on English text using Fuzzy Logics

Abstract

Sentiment analysis, a critical component of natural language processing, holds significant importance in deciphering public opinion, customer feedback, and user sentiments across diverse domains. This study introduces an innovative approach to sentiment analysis of English text utilizing fuzzy logic. Fuzzy logic is employed to tackle language ambiguity and vagueness. The methodology includes text preprocessing, linguistic variable creation via fuzzy membership functions, and fuzzy rules for sentiment classification. It provides nuanced sentiment scores, taking into account sentiment strength and context. The approach incorporates sentiment lexicons and machine learning for improved accuracy. The dataset of 50,000 English movie reviews is collected from Kaggle. Experiments demonstrate the superiority of fuzzy logic over traditional methods in handling the complexities of human language. The study also introduces a fuzzy architecture for sentiment analysis, achieving a 95% accuracy rate on a dataset of 100 movie reviews. Future work aims to enhance the approach by incorporating verbs, adjectives, and adverbs, further improving accuracy and precision in sentiment analysis.