Enhancing Mood and Energy Detection in NLP through Fuzzy Logic Integration

Abstract

This research presents a dual approach to textual analysis by utilizing fuzzy logic to detect both emotional and energy intensities in text. Through the use of trapezoidal membership functions to model diverse emotional and energy states and the application of an extensive set of fuzzy inference rules, the proposed methodology provides a nuanced, context-aware interpretation of moods and energy levels within text.