Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (11): 38-51    DOI: 10.11925/infotech.2096-3467.2022.0129
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Identifying Useful Reviews with Improved Graph Convolutional Neural Network
Li Xuemei,Jiang Jianhong()
Commercial College, Guilin University of Electronic Technology, Guilin 541004, China
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Abstract

[Objective] This paper tries to utilize the semantic deviation of comments, aiming to identify the useful online reviews. [Methods] We constructed an FFGCN model integrating chunk analysis and feature membership to evaluate the comments’ usefulness. Then, we utilized chunk analysis to obtain the feature and opinion chunks as nodes on the graph. Third, with the help of multi-granularity feature thesaurus, we integrated the membership relationship between feature words into the graph. Finally, we classified the comments through convolution on the graph. [Results] The recognition accuracy of the FFGCN model on the two datasets were 93.4% and 93.9%, which were 0.9 and 1.0 percentadge point higher than the optimal results of the baseline model. [Limitations] We only examined the new model with mobile phone review data. More research is needed to evaluate the model with data sets from other fields. [Conclusions] The proposed model can effectively identify the helpful products reviews online.

Received: 18 February 2022      Published: 13 January 2023
 ZTFLH: TP391
Fund:National Natural Science Foundation of China(71940008);MOE Project of Humanities and Social Sciences(17YJCZH074);Innovation Project of GUET Graduate Education(C21YJM00WX06)
Corresponding Authors: Jiang Jianhong     E-mail: jjhome@guet.edu.cn