用RoBERTa分析亚马逊评论情感，揭示消费者行为规律
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making
- 基于RoBERTa模型分析海量亚马逊评论情感
- 发现情感倾向与电子口碑、确认偏误等行为经济学原理高度相关
- 为营销决策和用户行为研究提供可落地的数据支持
本研究采用先进的自然语言处理技术，对亚马逊产品评论进行情感分析。通过使用基于Transformer的RoBERTa模型，分析大规模数据集以生成准确反映评论情绪倾向的情感评分。深入解析模型原理并评估其在情感评分生成中的表现。进一步开展全面的数据分析与可视化，识别情感评分中的模式与趋势，探究其与电子口碑（eWOM）、消费者情绪反应及确认偏误等行为经济学理论的一致性。结果表明，先进NLP模型在情感分析中具有显著有效性，并为理解消费者行为提供了宝贵洞见，对战略决策与营销实践具有重要意义。
原文摘要 · Abstract (English)
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.
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