Enhancing Mood and Energy Detection in NLP through Fuzzy Logic Integration

Authors

  • Halyna Melnyk Yuriy Fedkovych Chernivtsi National University
  • Vasyl Melnyk Yuriy Fedkovych Chernivtsi National University

DOI:

https://doi.org/10.31713/MCIT.2024.047

Keywords:

Fuzzy Logic, Mood Detection, Energy Intensity, Textual Analysis, Natural Language Processing, Semantic Analysis, Linguistic Variables, Fuzzification, Fuzzy Inference Rules

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.

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Published

2025-04-25

How to Cite

Melnyk, H., & Melnyk, V. (2025). Enhancing Mood and Energy Detection in NLP through Fuzzy Logic Integration. Modeling, Control and Information Technologies, (7), 165–168. https://doi.org/10.31713/MCIT.2024.047

Issue

No. 7 (2024): Modeling, control and information technologies

Section

Internet of Things (IoT) and artificial intelligence

License

Copyright (c) 2024 Modeling, Control and Information Technologies: Proceedings of International scientific and practical conference

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All materials are distributed under the terms of the Creative Commons Attribution 4.0 International License, which allows others to distribute the work with attribution to the authorship of this work and the first publication in this journal.