Integrating Fuzzy Logic and Natural Language Processing for Uncertainty Management in Sentiment Analysis

Abstract

The proliferation of social media and online platforms has led to an exponential increase in the amount of user-generated content. Sentiment analysis has become a popular technique for automatically extracting subjective information from such content, but traditional approaches often struggle with the inherent ambiguity and uncertainty in natural language. Fuzzy logic has emerged as a powerful tool for handling uncertainty, but its integration with natural language processing (NLP) techniques in sentiment analysis has received relatively little attention. In this paper, we propose an approach that combines fuzzy logic and NLP to improve uncertainty management in sentiment analysis. We present a novel fuzzy sentiment analysis model that incorporates linguistic hedges, intensifiers, and negators, which are commonly used to express degrees of uncertainty in natural language. We evaluate the proposed approach on several benchmark datasets and demonstrate its superior performance compared to traditional approaches. Our results show that the integration of fuzzy logic and NLP can significantly improve sentiment analysis accuracy, especially in cases where uncertainty and ambiguity are prevalent.