Enhanced Sentiment Interpretation via a Lexicon-Fuzzy-Transformer Framework
This work addresses the problem of fine-grained sentiment analysis for linguistically dynamic domains like food delivery and e-commerce, but it is incremental as it combines existing methods (lexicon-based, transformer, fuzzy logic) into a hybrid framework.
The authors tackled the challenge of accurately detecting sentiment polarity and intensity in product reviews and social media posts by proposing a hybrid lexicon-fuzzy-transformer framework, which resulted in improved alignment with user ratings, better identification of sentiment extremes, and reduced misclassifications across four domain-specific datasets.
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.