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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (3): 36-44    DOI: 10.11925/infotech.2096-3467.2018.0573
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Extracting Keywords from User Comments: Case Study of Meituan
Zhen Zhang1,Jin Zeng2()
1School of Information Management, Central China Normal University, Wuhan 430079, China
2School of Information Management, Wuhan University, Wuhan 430072, China
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[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, 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.

Key wordsProduction Recommendation      User Comments      Keywords Extraction     
Received: 22 May 2018      Published: 17 April 2019

Cite this article:

Zhen Zhang,Jin Zeng. Extracting Keywords from User Comments: Case Study of Meituan. Data Analysis and Knowledge Discovery, 2019, 3(3): 36-44.

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