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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (9): 22-30    DOI: 10.11925/infotech.2096-3467.2018.0015
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Selecting Products Based on F-BiGRU Sentiment Analysis
Bengong Yu1,2(),Peihang Zhang1,Qingtang Xu1
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
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Abstract  

[Objective] This paper proposes a product selection method based on the Feature Bidirectional Gated Recurrent Unit model (F-BiGRU), aiming to improve the efficiency of customers’ product selection and help them make better shopping decisions. [Methods] First, we retrieved online reviews for related products. Then, we categorized these online reviews in accordance with the product attributes. Third, we trained the F-BiGRU model using positive and negative reviews. Fourth, we quantified the sentiment of reviews on different attributes with the F-BiGRU model. Finally, we got the degrees of satisfaction on product attributes, and sorted the products using TOPSIS method. [Results] We retrieved the review texts on cars to conduct an empirical analysis. We found that the F-BiGRU method improved the accuracy of sentiment analysis, and is more appropriate for the short text reviews than traditional methods. [Limitations] The proposed deep learning model requires large dataset, which limits its performance with smaller datasets. [Conclusions] The product selection method based on F-BiGRU helps consumers choose needed products more efficiently.

Key wordsProduct Selection      Online Review      Sentiment Analysis      Deep Learning      Gated Recurrent Unit     
Received: 04 January 2018      Published: 25 October 2018

Cite this article:

Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis. Data Analysis and Knowledge Discovery, 2018, 2(9): 22-30.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0015     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I9/22

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