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数据分析与知识发现  2019, Vol. 3 Issue (1): 95-103    DOI: 10.11925/infotech.2096-3467.2018.0158
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于卷积神经网络的细粒度情感分析方法*
李慧,柴亚青()
西安电子科技大学经济与管理学院 西安 710071
Fine-Grained Sentiment Analysis Based on Convolutional Neural Network
Hui Li,Yaqing Chai()
School of Economics and Management, Xidian University, Xi’an 710071, China
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摘要 

【目的】提出一种基于卷积神经网络的细粒度情感分析方法。【方法】在词向量模型中融入属性特征, 从细粒度即产品或服务的属性特征角度出发, 采用统计学方法抽取评论文本的属性词集, 融合属性特征的影响差异性, 构建基于评论对象属性特征的文本特征向量, 采用包含多粒度卷积核的CNN模型进行训练。【结果】融合属性特征的多粒度卷积核CNN模型训练结果相较于传统情感分类模型和常规CNN模型在准确率、召回率和F-score评价指标方面均有显著提高。【局限】仅选取一个领域的评论集。【结论】基于卷积神经网络的细粒度情感分析方法可以进一步提高情感分类准确性。

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李慧
柴亚青
关键词 属性特征词向量情感分类CNN    
Abstract

[Objective] This paper proposes a fine-grained sentiment analysis method based on Convolutional Neural Network(CNN). [Methods] First, we incorporated attribute features into the word vector model. Then, we extracted the keyword sets of the comments statistically based on the fine-grained attributes of products or services. Third, we constructed the eigenvectors of the comments with attributes of the target objects. Finally, we trained the modified CNN model to add the affective clustering layer of the input text vector. [Results] Compared with the traditional emotion classification model, the training results of the new CNN model were significantly improved in terms of precision, recall and F-score. [Limitations] Only examined the new model with comments from one field. [Conclusions] The fine-grained sentiment analysis method based on convolutional neural network can dramatically improve the precision of sentiment classification.

Key wordsAttribute Feature    Word Vector    Sentiment Classification    CNN
收稿日期: 2018-02-07     
基金资助:*本文系国家自然科学青年基金项目“大规模动态社交网络社团检测算法研究”(项目编号: 71401130)的研究成果之一
引用本文:   
李慧,柴亚青. 基于卷积神经网络的细粒度情感分析方法*[J]. 数据分析与知识发现, 2019, 3(1): 95-103.
Hui Li,Yaqing Chai. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0158.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0158
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