面向主题的高质量评论挖掘模型研究

Research on Subject-Oriented High Quality Reviews Mining Model

【目的】帮助消费者从海量的评论集合中识别高质量评论。【方法】利用LDA主题模型对消费者关注的主题进行分类,借鉴改进的自动摘要的思想,追踪评论主题下的高质量评论,提出面向主题的高质量评论挖掘模型。【结果】自动提炼出每个主题下的高质量评论,其准确率、召回率和F1值分别为80.73%、64.90%和71.95%,并通过实证研究证明该模型的有效性和优越性。【局限】仅与部分典型模型作对比,其他模型方法还未进行验证。【结论】该模型能从评论集中有效地挖掘出不同主题下的高质量评论,从而能够更加高效地辅助消费者进行购买决策。

[Objective] In order to help consumers distinguish high quality reviews from enormous review sets. [Methods] Using LDA topic model to classify the themes and referring to the thoughts of improved automatic summarization, this paper puts forward Subject-Oriented High Quality Reviews Mining Model. [Results] The model extracts high quality reviews automatically under each topic. The results of the experiment show that its precision, recall and F1 score reach 80.73%, 64.90% and 71.95% respectively, proving the model＇s effectiveness and superiority. [Limitations] Just compared the model with some typical models, but there are some other methods exist but have not been verified. [Conclusions] The model can effectively mine high quality reviews under different themes from the review sets, thus help customers in making more effective purchase decision.