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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (12): 74-83    DOI: 10.11925/infotech.2096-3467.2017.0866
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Identifying Useful Online Reviews with Semantic Feature Extraction
Yanfeng Zhang(),He Li,Lihui Peng,Litie Hou
School of Management, Jilin University, Changchun 130022, China
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Abstract  

[Objective] We propose a model to identify useful online Chinese reviews, which helps consumers make purchasing decisions. [Methods] First, we calculated six attributes affecting the usefulness of online reviews based on their form and content characteristics. Then, we constructed a usefulness evaluation system with the weighted grey relational degree analysis method. Finally, we created a model to retrieve useful online reviews with k-means clustering method. [Results] We examined the effectiveness of our model with online reviews from Amazon.com. The recall, precision and F values showed that our method could effectively identify the useful online reviews, and classify the polarity ones. [Limitations] The samples, metrics and e-commerce platforms could be further improved. [Conclusions] The proposed method could rank and classify online reviews accurately and reliably.

Key wordsWeighted Grey Relational Degree      Online Reviews      Classification Model      Usefulness     
Received: 28 August 2017      Published: 29 December 2017

Cite this article:

Yanfeng Zhang,He Li,Lihui Peng,Litie Hou. Identifying Useful Online Reviews with Semantic Feature Extraction. Data Analysis and Knowledge Discovery, 2017, 1(12): 74-83.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0866     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I12/74

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