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数据分析与知识发现  2020, Vol. 4 Issue (2/3): 207-213     https://doi.org/10.11925/infotech.2096-3467.2019.0678
  专辑 本期目录 | 过刊浏览 | 高级检索 |
一种基于CRF与ATAE-LSTM的细粒度情感分析方法*
薛福亮(),刘丽芳
天津财经大学商学院 天津 300222
Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM
Xue Fuliang(),Liu Lifang
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
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摘要 

【目的】 应用细粒度情感分析方法提取产品属性及情感,进而将属性词聚类到属性面,分析用户在产品属性面的情感。【方法】 通过CRF抽取产品属性词,利用基于注意力机制的长短期记忆网络做属性情感分析,最后基于Word2Vec将属性词聚集为属性面,并分析电商平台产品属性面的情感。【结果】 CRF抽取属性词的F1值为0.76,ATAE-LSTM属性情感分析的F1值为0.78。【局限】 只抽取显式属性词,对隐式属性词抽取效果较差;数据集偏小。【结论】 通过对属性词的抽取、情感分析以及属性面聚类,可较好地解释用户对产品的属性偏好。

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薛福亮
刘丽芳
关键词 CRF长短期记忆网络注意力机制情感分析Word2Vec    
Abstract

[Objective] This paper tries to extract product attributes, aiming to cluster these words and analyze user’s sentiments.[Methods] Firstly, we identified the attributes of products with CRF technique. Then, we analyzed the sentiment of extracted terms with attention-based LSTM. Finally, we clustered these terms into appropriate categories with the help of Word2Vec and conducted fine-grained sentiment analysis of the products.[Results] The F1 values of term extraction and sentiment analysis were 0.76 and 0.78.[Limitations] We only retrieved explicit terms for this study and the sample size needs to be expanded.[Conclusions] The proposed method could effectively explore user’s preference in products.

Key wordsCRF    LSTM    Attention Mechanism    Sentiment Analysis    Word2Vec
收稿日期: 2019-06-14      出版日期: 2020-04-26
ZTFLH:  TP391  
基金资助:*本文系天津市哲学社会科学规划项目“基于电子商务用户评论文本的领域情感词典构建方法研究”的研究成果之一(TJGL19-009)
通讯作者: 薛福亮     E-mail: fuliangxue@163.com
引用本文:   
薛福亮,刘丽芳. 一种基于CRF与ATAE-LSTM的细粒度情感分析方法*[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 207-213.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0678      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I2/3/207
Fig.1  研究框架
Fig.2  ATAE-LSTM结构
Fig.3  Skip-gram模型
评论 Pos Tag 评论 Pos Tag
运行/ v B 也/ d O
速度/ n I 很/ d O
快/ a O 耐用/ a O
,/ x O x O
电池/ n B
Table 1  属性词标记示例
Fig.4  不同聚类数目K下的欧氏距离变化趋势
Fig.5  属性词聚类结果
实验 P R F1
基于CRF抽取属性词
基于关联规则抽取属性词
0.84
0.48
0.70
0.14
0.76
0.21
基于ATAE-LSTM属性情感分析
基于LSTM属性情感分析
0.78
0.71
0.81
0.79
0.78
0.73
Table 2  实验结果
属性面 属性词 正面情感 中性情感 负面情感
设计 设计 75% 0 25%
外形与功能 信号 4% 96% 0
相机 75% 0 25%
外形 89% 0 11%
摄像头 9% 0 91%
速度 充电速度 100% 0 0
系统速度 100% 0 0
Table 3  部分属性面的情感指标
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