Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization

NSAR-T: A Fine-Grained Aspect-Based Sentiment and Transformer Framework for Personalized Recommendations

The rapid growth of digital platforms and online review systems has significantly increased the need for intelligent recommendation systems capable of understanding user preferences and contextual sentiments. However, traditional recommendation models often fail to capture fine-grained emotional variations, aspect-level opinions, and semantic relationships present in textual reviews, leading to reduced personalization accuracy. This work proposes a Nuanced Sentiment-Aware Recommendation with Transformers (NSAR-T) model incorporating Aspect-Based Sentiment Analysis (ABSA) with the Robustly Optimized Bidirectional Encoder Representation from Transformers (RoBERTa) to enable the generation of more granular personalized recommendations. A framework for hotel recommendations based on a combination of data sources consisting of 200,000 scraped review records from the primary source of hotels, 878,561 TripAdvisor reviews, and 37.6 million records for Expedia recommendations. The methodology consists of data preparation, extraction of aspects, sentiment classification based on RoBERTa, seven varieties of fine-grained modelling of sentiment, aggregation of sentiment, Principal Component Analysis (PCA) based feature engineering, Cross-Validation (CV), and generation of recommendations from the Top-10 list. The results show an accuracy of 99.85%, precision of 99.91%, recall of 99.81%, F1-Score of 99.86%, Root Mean Square Error (RMSE) of 0.03, Mean Average Precision (MAP) of 0.99, and Normalized Discounted Cumulative Gain (NDCG)@10 of 1; demonstrating superiority over currently employed machine learning and transformer-based recommendation systems with regard to the relevance of recommendations, contextual knowledge of recommendations, and how personalized recommendations are created.

Sentiment-aware Rating-based Recommendation via Semantic-enhanced Item Alignment

Transformer-Based Topic Modeling and Sentiment Analysis for Multilingual Text Data

An integrated framework based on transformer architecture for topic modeling and sentiment analysis for Hindi and Italian social-media discourse, customer reviews and news corpus is introduced and it is suggested that there is clear benefit for morphologically complex text and mixed script text for using contextual embeddings and language-specific pretraining.

Reliability Evaluation of Large Language Models for Social Media Sentiment Annotation: An Empirical Study Based on Model Agreement and Downstream Tasks

Obvious performance discrepancies exist across LLMs; high-quality annotations significantly boost downstream classification accuracy, which highlights the importance of selecting competent LLMs for sentiment labeling tasks.

Evaluating Summaries of Social Media Posts: Balancing Fairness for Readers and Authors

Text summarization has a wide range of applications, from condensing lengthy documents to summarizing user-generated content (such as tweets, Facebook posts, and Reddit discussions) that reflect diverse viewpoints on socially significant topics. Traditionally, summarization has been considered a reader-centric task, and algorithms are evaluated against human-written reference summaries using metrics such as the ROUGE score. However, this approach has two primary limitations when applied to user-generated content, especially written on sensitive issues. First, through surveys with participants from various countries, we demonstrate that human-written reference summaries can contain implicit biases. These biases introduce significant variations in the evaluation of summarization algorithms, raising concerns about their reliability in identifying summaries that effectively meet reader expectations. Second, we show that algorithmically generated summaries, as ranked by traditional evaluation methods, often fail to align with authors’ preferences. These issues render the existing summary evaluation framework suboptimal for both readers and the authors of the content being summarized. In this work, by reimagining summarization of user-generated content as a two-sided problem, we emphasize the importance of satisfying both readers and authors. Specifically, we take a two-pronged approach to tackle the issues with existing evaluation setup: (i) we introduce the concept of inequality in reader satisfaction to tackle the implicit biases in reference summaries; and (ii) we propose a novel evaluation framework based on satisfying the authors of the content. More specifically, we present an author satisfaction-based evaluation metric CROSSEM which, we show empirically, can complement the current summary evaluation paradigm. This work represents the first attempt to develop a fair summary evaluation framework for social media posts and is likely to spark future research in this domain.

Rating-aware argument generation for movie reviews with multimodal large language models and a new dataset