TL;DR
This paper introduces a unified framework for explainable rating prediction and sentiment-segmented summarization, which employs a neural network for accurate rating prediction and a Retrieval-Augmented Generation module for interpretable justifications of predicted ratings.
Recent research in explainable recommendation commonly uses natural language explanations to improve transparency and user trust. However, reliably evaluating whether explanations are semantically faithful to users’ multi-dimensional preferences remains challenging. Existing methods mainly rely on text similarity metri...
Sentimental analysis (SA) of movie reviews has become an essential means of supporting audiences, filmmakers, and investors to facilitate data-driven marketing, support audience engagement, and maximize audience development returns. Nevertheless, current SA models are yet to overcome potentially daunting problems suc...
This empirical study compares performance of ChatGPT, Claude AI, and BARD to model topics on 547 Amazon reviews of 2 water filter brands and shows that LDA produced more coherent, detailed themes, while LLM-based topic modeling generated broader, more semantically diffused clusters, highlighting a trade-off between int...
The proposed SA-RMMR framework represents an efficient and interpretable approach to customer review summarization that takes into account not only semantic relevance but also product aspects, sentiment preservation, and redundant information filtering.
Sentiment information can enhance path-based explainable recommendation, yet advances are hindered by the lack of public benchmarks for sentiment-aware knowledge graph (SAKG) reasoning. We address this gap by systematically evaluating how different sentiment representation strategies and reward mechanisms affect utilit...
GovBERTic (Government BERTopic), a reproducible computational pipeline that operationalizes public opinion analysis by linking semantic topic discovery, multi-algorithm sentiment model selection, and topic-level sentiment aggregation within a single workflow, is developed.
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