| C. Troussas, C. Papakostas , A. Krouska, Ph. Mylonas, C. Sgouropoulou |
| Fuzzy-Weighted Sentiment Recognition for Educational Text-based Interactions |
| 21st International Conference on Web Information Systems and Technologies (WEBIST 2025), Marbella, Spain, October 21-23, 2025 |
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ABSTRACT
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| In web-based educational environments, students often express complex emotional states – such as confusion, frustration, or engagement – through reflective texts, forum posts, and peer interactions. Traditional sentiment analysis tools struggle to capture these subtle, mixed signals due to their reliance on rigid classification schemes and lack of domain sensitivity. To address this, we propose a fuzzy-weighted sentiment recognition framework designed specifically for educational text-based interactions. The system combines an augmented sentiment lexicon, rule-based modifier detection, and semantic similarity using pretrained Sentence-BERT embeddings to extract nuanced sentiment signals. These inputs are interpreted by a Mamdani-type fuzzy inference engine, producing a continuous sentiment score and a confidence weight that reflect both the strength and reliability of the learner¢s affective state. The paper details the linguistic pipeline, fuzzy membership functions, inference rules, and aggregation strategies that enable interpretable and adaptive sentiment modeling. Evaluation on a corpus of 1125 annotated student texts from a university programming course shows that the proposed system outperforms both lexicon-based and deep learning baselines in accuracy, robustness, and interpretability, demonstrating its value for affect-aware educational applications.
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| 21 October , 2025 |
| C. Troussas, C. Papakostas , A. Krouska, Ph. Mylonas, C. Sgouropoulou, "Fuzzy-Weighted Sentiment Recognition for Educational Text-based Interactions", 21st International Conference on Web Information Systems and Technologies (WEBIST 2025), Marbella, Spain, October 21-23, 2025 |
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