A Neuro-Fuzzy Framework for Sentiment Analysis on Noisy Social Media Data

Abstract

The proliferation of social media platforms has led to an unprecedented volume of user-generated content containing rich sentiment data. However, this data is often noisy, ambiguous, and linguistically inconsistent, posing challenges to traditional sentiment analysis models. This paper proposes a neuro-fuzzy framework that synergistically combines the learning capabilities of neural networks with the approximate reasoning of fuzzy logic. The objective is to improve sentiment classification accuracy by handling linguistic vagueness and uncertainty inherent in social media text. The proposed system preprocesses noisy inputs, extracts semantic features, and applies a hybrid model trained on labeled datasets. Comparative analysis with standard machine learning models shows superior performance in dealing with ambiguous sentiment cases. This approach demonstrates the potential of soft computing techniques in AI-based sentiment analysis tasks for real-world applications such as brand monitoring, political opinion mining, and crisis management.

KEYWORDS: Sentiment Analysis, Neuro-Fuzzy Systems, Social Media Analytics, Fuzzy Logic, Deep Learning, Soft Computing, Natural Language Processing, Text Classification, Noisy Data Handling, Artificial Intelligence