Enhancing Sentiment Analysis with Term Sentiment Entropy: Capturing Nuanced Sentiment in Text Classification
Open accessAbstract
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.
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This limitation is particularly evident in complex language tasks, such as intricate question answering or nuanced sentiment analysis.
“This limitation is particularly evident in complex language tasks, such as intricate question answering [151] or nuanced sentiment analysis [152].”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [152]
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Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works
- Authors
- Klongdee, S., & Singthongchai, J.
- Affiliations
- Rajamangala University of Technology Tawan-ok (TH)
- Kalasin University (TH)
- Corresponding author
- Singthongchai, Jatsada
- Venue
- Preprints.org
- Publisher
- MDPI AG
- Published
- 2024-03-22
- OpenAlex
- W4393154964
- Cited by
- 3
- References
- 25
- Citations by year
- 2025: 2 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Preprints.org (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- sentiment analysis, text classification, term weighting, TF-IDF, domain adaptation, customer feedback
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