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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
Yu Bengong1,2(), Zhang Peihang1, Xu Qingtang1
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
ZTFLH:  分类号: C931.6  

Cite this article:

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

URL:

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

方法 准确率 召回率 F1值
NB 91.18% 92.21% 91.69%
SVM 93.60% 87.93% 90.68%
NN 92.39% 93.27% 92.83%
RF 91.52% 86.62% 89.00%
CNN 96.91% 94.87% 95.88%
GRU 96.17% 97.23% 96.70%
F-BiGRU 98.05% 97.18% 97.61%
车型 均价(万) 口碑评论文本数量(条)
瑞虎7 13 3 755
思域 14 2 171
荣威i6 12 1 665
明锐 15 1 390
轩逸 13 1 387
朗逸 14 3 127
博越 13 4 439
荣威RX5 15 6 374
车型 空间 动力 操控 油耗 舒适性 外观 内饰
瑞虎7 0.82664692 0.79215884 0.85923731 0.61679804 0.7480377 0.93323213 0.66287327
思域 0.751369 0.77653182 0.57529676 0.60946149 0.40987739 0.80474067 0.33736464
荣威i6 0.87899387 0.78861713 0.81631488 0.75512266 0.49496683 0.92696506 0.57103211
明锐 0.82699454 0.32145673 0.77134079 0.65305305 0.43916675 0.79899246 0.53765839
轩逸 0.80659056 0.37046233 0.54579508 0.69502336 0.67606455 0.70648897 0.49541172
朗逸 0.81036848 0.3259283 0.7747519 0.71296167 0.43799475 0.83163321 0.2792967
博越 0.6879878 0.74954957 0.82120782 0.33669931 0.75716293 0.84331733 0.79239517
荣威RX5 0.90069151 0.71247077 0.78634995 0.60119843 0.56585765 0.90103316 0.67857999
空间 动力 操控 油耗 舒适性 外观 内饰
0.063517 0.156486 0.183341 0.09252 0.303589 0.166057 0.034488
车型 d* d 0 c 排序
瑞虎7 0.0146127 0.1470008481 0.9095824745 1
博越 0.04468999 0.1377778628 0.7550802033 2
荣威RX5 0.06288051 0.0997304454 0.6133070570 3
荣威i6 0.08037187 0.1075123034 0.5722264991 4
轩逸 0.09925965 0.0883214247 0.4708440076 1
思域 0.12167597 0.0776130979 0.3894498509 2
朗逸 0.12489039 0.0594943325 0.3226641031 3
明锐 0.1252614 0.0551273065 0.3056028650 4
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