Performance Improvement using Fuzzy String Matching Based Features for Sentiment Analysis on Social Media Data

Abstract

This paper presents Nested Social Sentiments Classification (NeSS-Class), a sentiment analysis framework that incorporates both primary posts and their nested comments from social media platforms. Unlike traditional approaches, NeSS-Class introduces derived features based on fuzzy string matching to capture subtle textual similarities and to address challenges arising from complex user interaction behaviors in nested discussions. Data was gathered from multiple social media platforms, carefully preprocessed it, and divided into training, validation, and testing sets in an 80-10-10 ratio. Feature stability was evaluated using univariate analysis, and baseline machine learning models were employed for performance assessment. Experimental results demonstrate that Logistic Regression integrated with NeSS-Class significantly improves classification performance, achieving a log-loss value of 0.6553, compared to 0.9099 obtained without the proposed feature set. These results confirm the effectiveness of fuzzy string matching as a feature engineering strategy for enhancing sentiment analysis in noisy, multi-layered social media data.