%A Zhen Zhang,Jin Zeng %T Extracting Keywords from User Comments: Case Study of Meituan %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.0573 %P 36-44 %V 3 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4627.shtml} %8 2019-03-25 %X

[Objective] This paper tries to automatically extract keywords from user comments, aiming to help both buyers and sellers find valuable information. It supports the decision making of customers and provides feedbacks to improve online services. [Methods] Firstly, we defined the task of extracting keywords from user comments. Then, we proposed evaluation criteria from the perspectives of merchants and customers. Thirdly, we constructed a language model based keyword extraction method (LMKE). Finally, we collected experimental data from Meituan.com, and compared the performance of our method with two existing ones, i.e., TF-IDF and TextRank. [Results] The scores of our LMKE method were 0.7665, 0.6701, 0.6200, 0.8187, 0.7326 and 0.6743 with P@5, P@10, P@20, nDCG@5, nDCG@10 and nDCG@20. [Limitations] Our dataset was only built with user’s comments on buffet services in Wuhan, China. [Conclusions] The discriminative LMKE model has better performance than those of the TF-IDF and TextRank.