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数据分析与知识发现  2018, Vol. 2 Issue (9): 22-30     https://doi.org/10.11925/infotech.2096-3467.2018.0015
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于F-BiGRU情感分析的产品选择方法*
余本功1,2(), 张培行1, 许庆堂1
1合肥工业大学管理学院 合肥 230009
2过程优化与智能决策教育部重点实验室 合肥 230009
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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摘要 

【目的】为提高产品选择效率, 帮助消费者更好地制定购物决策, 本文在门限递归单元的基础上, 提出一种特征强化双向门限递归单元模型(Feature Bidirectional Gated Recurrent Unit, F-BiGRU)。【方法】首先, 获取相关产品的在线评论信息; 然后对在线评论按照产品属性进行分割; 使用正向情感评论和负向情感评论对F-BiGRU模型进行训练; 最后使用F-BiGRU模型对产品各属性的评论进行情感量化, 得到产品各属性的情感满意程度, 并使用TOPSIS法对候选产品进行排序。【结果】选取汽车口碑文本评论数据进行实证, 对比相关情感分析方法, F-BiGRU方法提高了情感分析的准确度, 更适应在线评论短文本的特点。【局限】深度学习模型需要大规模的数据集, 本文方法在一些小数据集上的表现可能不佳。【结论】基于F-BiGRU情感分析的产品选择方法提高了情感分析的准确度, 能更高效快捷地帮助消费者进行产品选择。

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余本功
张培行
许庆堂
关键词 产品选择在线评论情感分析深度学习门限递归单元    
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
收稿日期: 2018-01-04      出版日期: 2018-10-25
ZTFLH:  分类号: C931.6  
基金资助:*本文系国家自然科学基金项目“基于制造大数据的产品研发知识集成与服务机制研究”(项目编号: 71671057)的研究成果之一
引用本文:   
余本功, 张培行, 许庆堂. 基于F-BiGRU情感分析的产品选择方法*[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
Yu Bengong,Zhang Peihang,Xu Qingtang. Selecting Products Based on F-BiGRU Sentiment Analysis. Data Analysis and Knowledge Discovery, 2018, 2(9): 22-30.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0015      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I9/22
  GRU模型结构
  基于F-BiGRU情感分析的产品选择方法
  F-BiGRU模型结构图
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%
  各模型分类评价对比
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
  候选车型价格及评论数量
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
  候选车型各属性满意程度
  汽车产品属性满意程度图
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
  属性权重
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
  候选车型TOPSIS排序
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