Download PDFOpen PDF in browserUnveiling Fibromyalgia Research Frontiers: Transformer-Based Topic and Sentiment Modeling for Biomedical Meta-Analysis10 pages•Published: April 19, 2026AbstractThe exponential growth of biomedical literature poses challenges for synthesizing thematic and emotional insights, particularly in underexplored conditions like fibromyalgia. We present a reproducible and modular pipeline that integrates BERTopic — an explainable topic modeling framework — with sentiment analysis to map 5,861 PubMed abstracts on fibromyalgia. It primarily spans publications from 1990 to 2020, with a small number of records predating 1990; this coverage enables longitudinal analysis of research themes and sentiment. Our approach combines Sentence-BERT embeddings, density-based clustering, and TF-IDF topic representation to extract 111 interpretable topics and one noise cluster. Key themes include sleep dysfunction, multimodal treatment, genetic biomarkers, and patient experience—an emergent area increasingly emphasized in chronic illness research. We benchmark BERTopic against Latent Dirichlet Allocation (LDA) and Contextual Topic Modeling (CTM) using four coherence metrics (C_V, UMass, NPMI, and C_UCI). While CTM achieved the highest coherence score (C_V = 0.6748), BERTopic (C_V = 0.6331) offered superior visualization, adaptability, and usability. Sentiment analysis, conducted using a DistilBERT classifier trained on the SST-2 dataset, revealed domain-specific polarity patterns — e.g., overwhelmingly negative tone in sleep-related studies and balanced sentiment in patient-centered topics. Although the sentiment model was not fine-tuned on biomedical text, it provided meaningful first-order approximations. This work contributes a scalable framework for scientific landscape mapping in low-data medical domains. We discuss limitations—including the presence of noise (Topic -1) and reliance on abstracts—and outline future directions such as domain-specific sentiment fine-tuning and full-text integration.Keyphrases: artificial intelligence in healthcare, biomedical natural language processing, fibromyalgia, scientific workflow automation, sentiment analysis, topic modeling In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 208-217.
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