A Neutrosophic Approach to Improving Sentiment Classification Accuracy in Social Media Analytics - Raad A. Qasim; Sajjad abbas; Habeeb Noori Jumaah; Maher Khalaf Hussein; Huda E. Khalid (Neutrosophic Sets and Systems)
Traditional sentiment analysis methods often struggle with the inherent ambiguity and uncertainty present in social media text, where opinions can be simultaneously positive, negative, and neutral. This paper proposes a novel neutrosophic-based approach to sentiment classification that addresses the limitations of binary and ternary classification systems. By incorporating neutrosophic logic's three-valued framework (truth, indeterminacy, and falsity), our method better captures the nuanced nature of social media sentiment expressions. Experimental results on multiple social media datasets demonstrate significant improvements in classification accuracy, with our neutrosophic approach achieving 15% to 20% better performance in handling ambiguous and mixed-sentiment posts compared to conventional methods. The proposed framework shows particular effectiveness in processing sarcastic, ironic that are contextually dependent expressions common in social media platforms.