Version 1
: Received: 21 March 2024 / Approved: 22 March 2024 / Online: 22 March 2024 (14:35:34 CET)
How to cite:
Klongdee, S.; Singthongchai, J. Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification. Preprints2024, 2024031364. https://doi.org/10.20944/preprints202403.1364.v1
Klongdee, S.; Singthongchai, J. Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification. Preprints 2024, 2024031364. https://doi.org/10.20944/preprints202403.1364.v1
Klongdee, S.; Singthongchai, J. Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification. Preprints2024, 2024031364. https://doi.org/10.20944/preprints202403.1364.v1
APA Style
Klongdee, S., & Singthongchai, J. (2024). Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification. Preprints. https://doi.org/10.20944/preprints202403.1364.v1
Chicago/Turabian Style
Klongdee, S. and Jatsada Singthongchai. 2024 "Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification" Preprints. https://doi.org/10.20944/preprints202403.1364.v1
Abstract
Sentiment analysis plays a crucial role in understanding customer feedback, guiding product development, and informing business decisions. This paper introduces term sentiment entropy (TSE), a novel weighting method that leverages the distribution of sentiment labels associated with words to enhance text classification accuracy. TSE complements traditional TF-IDF techniques by capturing the nuances of sentiment variation across different contexts. Experiments across diverse public datasets demonstrate TSE's potential to improve sentiment analysis performance, especially in capturing subtle sentiment shifts and adapting to specific domains. While computational cost and label quality pose challenges, TSE offers a promising avenue for refining sentiment analysis and opening new research frontiers.
Keywords
sentiment analysis; term weighting; text classification; TF-IDF; TFRF; natural language processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
