Beyond Binary Sentiment

Abstract

The ambiguity and gradience of natural human language are frequently difficult for conventional sentiment analysis models to capture, particularly in crucial situations like false information pertaining to health. This chapter explores how fuzzy linguistic hedges, like “somewhat,” “extremely,” and “not,” can improve sentiment analysis's semantic depth. We present a sophisticated sentiment-aware classification pipeline designed for fake news detection by fusing fuzzy logic with contemporary deep learning architectures (LSTM). Our model, when applied to Covid-19 infodemic datasets, not only increases classification accuracy but also offers more comprehensible insights into the emotional tone of factual versus misleading content. We also go over how fuzzy sentiment scoring affects more general NLP uses like automated fact-checking systems, policy communication, and social media monitoring