用大模型把用户评论变成交给企业的可执行建议
ReviewSense: Transforming Customer Review Dynamics into Actionable Business Insights
- 用聚类+大模型+专家评估构建企业可用的推荐流水线
- 能识别出重复问题和具体关切点，指导业务改进
- 适合需要从评论中提取行动方案的企业决策者
随着客户反馈日益成为战略增长的核心，从非结构化评论中提取可操作洞察变得至关重要。尽管传统AI系统在预测用户偏好方面表现优异，但较少研究关注如何将客户评论转化为面向业务的指导性建议。本文提出ReviewSense，一个基于大语言模型的预测性决策支持框架，能够将客户评论转化为精准、可执行的商业建议。通过识别客户情感中的关键趋势、重复性问题和具体关切，该框架超越了仅依赖偏好的系统，为维持增长和提升客户忠诚度提供深层洞见。其创新性在于将聚类、LLM适配与专家评估整合进统一的业务导向流程。初步人工评估显示，模型建议与企业目标高度一致，展现出推动数据驱动决策的潜力。该框架为人工智能驱动的情感分析提供了新视角，证明了其在优化商业策略和最大化客户反馈价值方面的应用价值。
原文摘要 · Abstract (English)
As customer feedback becomes increasingly central to strategic growth, the ability to derive actionable insights from unstructured reviews is essential. While traditional AI-driven systems excel at predicting user preferences, far less work has focused on transforming customer reviews into prescriptive, business-facing recommendations. This paper introduces ReviewSense, a novel prescriptive decision support framework that leverages advanced large language models (LLMs) to transform customer reviews into targeted, actionable business recommendations. By identifying key trends, recurring issues, and specific concerns within customer sentiments, ReviewSense extends beyond preference-based systems to provide businesses with deeper insights for sustaining growth and enhancing customer loyalty. The novelty of this work lies in integrating clustering, LLM adaptation, and expert-driven evaluation into a unified, business-facing pipeline. Preliminary manual evaluations indicate strong alignment between the model's recommendations and business objectives, highlighting its potential for driving data-informed decision-making. This framework offers a new perspective on AI-driven sentiment analysis, demonstrating its value in refining business strategies and maximizing the impact of customer feedback.
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