2024/10/18 by Xinli Guo, Guo, Xinli
Computer Science · Medicine · #Advanced Text Analysis Techniques #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Diverse Approaches in Healthcare and Education Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Technology and Data Analysis
paper · pdf · doi:10.48550/arxiv.2411.00796
openalex publication_date 2024/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this study, we leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. By employing transformer-based models, RoBERTa, we analyze a vast dataset to derive sentiment scores that accurately reflect the emotional tones of the reviews. We provide an in-depth explanation of the underlying principles of these models and evaluate their performance in generating sentiment scores. Further, we conduct comprehensive data analysis and visualization to identify patterns and trends in sentiment scores, examining their alignment with behavioral economics principles such as electronic word of mouth (eWOM), consumer emotional reactions, and the confirmation bias. Our findings demonstrate the efficacy of advanced NLP models in sentiment analysis and offer valuable insights into consumer behavior, with implications for strategic decision-making and marketing practices.