Enhanced Sentiment Interpretation via a Lexicon-Fuzzy-Transformer Framework

• URL: http://arxiv.org/abs/2510.15843v1
• Date: Fri, 17 Oct 2025 17:36:05 GMT
• Title: Enhanced Sentiment Interpretation via a Lexicon-Fuzzy-Transformer Framework
• Authors: Shayan Rokhva, Mousa Alizadeh, Maryam Abdollahi Shamami,
• Abstract summary: We propose a novel lexicon-fuzzy-transformer framework that combines rule-baseds, contextual deep learning, and fuzzy logic.<n>The framework is rigorously evaluated on four domain-specific datasets.<n>Results show improved alignment with user ratings, better identification of sentiment extremes, and reduced misclassifications.
• Score: 0.9558392439655014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately detecting sentiment polarity and intensity in product reviews and social media posts remains challenging due to informal and domain-specific language. To address this, we propose a novel hybrid lexicon-fuzzy-transformer framework that combines rule-based heuristics, contextual deep learning, and fuzzy logic to generate continuous sentiment scores reflecting both polarity and strength. The pipeline begins with VADER-based initial sentiment estimations, which are refined through a two-stage adjustment process. This involves leveraging confidence scores from DistilBERT, a lightweight transformer and applying fuzzy logic principles to mitigate excessive neutrality bias and enhance granularity. A custom fuzzy inference system then maps the refined scores onto a 0 to 1 continuum, producing expert)like judgments. The framework is rigorously evaluated on four domain-specific datasets. food delivery, e-commerce, tourism, and fashion. Results show improved alignment with user ratings, better identification of sentiment extremes, and reduced misclassifications. Both quantitative metrics (distributional alignment, confusion matrices) and qualitative insights (case studies, runtime analysis) affirm the models robustness and efficiency. This work demonstrates the value of integrating symbolic reasoning with neural models for interpretable, finegrained sentiment analysis in linguistically dynamic domains.

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