用情感单元重构评论分析，提升话题建模与评分预测准确率
TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction
- 将评论拆解为带情感分的语义单元，提升主题建模精度
- 生成可解释的主题并关联用户评分，揭示痛点对业务的影响
- 适合需深度理解客户反馈的企业分析场景
我们通过将话题建模流程重构为基于意见单元（opinion units）的操作，改进客户评论的洞察提取。这些单元包含相关文本片段及对应的情感得分，可由大语言模型可靠提取。该方法显著提升了后续话题建模的效果，生成连贯且可解释的主题，同时保留每个主题的情感信息。通过将主题与情感关联到业务指标（如星评），可识别具体客户关切对业务结果的影响。本文展示了系统的实现、应用场景及相较于其他话题建模与分类方案的优势，并评估了其生成一致主题的能力，以及融合话题与情感模态以实现精准评分预测的方法。
原文摘要 · Abstract (English)
We improve the extraction of insights from customer reviews by restructuring the topic modelling pipeline to operate on opinion units - distinct statements that include relevant text excerpts and associated sentiment scores. Prior work has demonstrated that such units can be reliably extracted using large language models. The result is a heightened performance of the subsequent topic modeling, leading to coherent and interpretable topics while also capturing the sentiment associated with each topic. By correlating the topics and sentiments with business metrics, such as star ratings, we can gain insights on how specific customer concerns impact business outcomes. We present our system's implementation, use cases, and advantages over other topic modeling and classification solutions. We also evaluate its effectiveness in creating coherent topics and assess methods for integrating topic and sentiment modalities for accurate star-rating prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。