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数据分析与知识发现  2019, Vol. 3 Issue (4): 71-79    DOI: 10.11925/infotech.2096-3467.2018.0516
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
1西安工程大学管理学院 西安 710048
2西安工程大学理学院 西安 710048
3西安交通大学新闻与新媒体学院 西安 710049
Text Sentiment Classification Based on Deep Belief Network
Qingqing Zhang1(),Xingshi He2,Huimin Wang2,Shengjun Meng3
1School of Management, Xi’an Polytechnic University, Xi’an 710048, China
2School of Science, Xi’an Polytechnic University, Xi’an 710048, China
3School of Journalism and New Media, Xi’an Jiaotong University, Xi’an 710049, China
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【目的】将深度信念网络应用于中文文本情感分类, 系统研究深度信念网络在文本情感分类任务中的参数选择与性能分析。【方法】以中文电子商务网站评论数据为研究对象, 提取一元词、二元词、词性、简单依存关系、情感得分和三元组依存关系特征作为深度信念网络的输入, 通过设置不同网络深度、不同输入维数的网络结构计算分类准确率。【结果】实验结果表明, 三元组依存关系特征作为深度信念网络的输入分类效果更好, 而网络层数对分类准确率的影响不大。【局限】尚未在其他深度学习模型上进行实验验证。【结论】深度学习在文本情感分类任务中性能良好, 验证了深度学习对复杂任务有很强的学习能力, 但其模型选择和参数设置尚需要进一步的研究。

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关键词 深度信念网络文本情感分类参数选择    

[Objective] This paper focused on Chinese text sentiment classification based on deep belief network, especially the parameter selection and performance analysis of the network. [Methods] Chinese e-commercial reviews are as the object of the study, the unigram, bigram, POS, simple dependency label, sentiment score and triple dependency features are extracted and used as the input of deep belief network by setting different layers and different input numbers to compute the accuracy of sentiment classification. [Results] The results demonstrate that the triple dependency features as the input got better classification performance than the other features, but the number of hidden layers doesn’t have an effect on the classification accuracy. [Limitations] The methods aren’t conducted and verified on other deep learning models. [Conclusions] Deep learning has a good performance for sentiment analysis, but how to set up parameters still need to be further considered.

Key wordsDeep Belief Network    Text Sentiment Classification    Parameter Selection
收稿日期: 2018-05-08     
基金资助:*本文系教育部人文社会科学青年基金项目“社会媒体网络社群对城市弱势群体公共事务参与的影响研究”(项目编号: 18YJC860025)、西安工程大学博士科研启动金“基于深度学习的中文文本情感分类研究”(项目编号: 107020309)和2019年陕西省教育厅科研计划专项项目“基于深度学习的情感分类研究”的研究成果之一
张庆庆,贺兴时,王慧敏,蒙胜军. 基于深度信念网络的文本情感分类研究*[J]. 数据分析与知识发现, 2019, 3(4): 71-79.
Qingqing Zhang,Xingshi He,Huimin Wang,Shengjun Meng. Text Sentiment Classification Based on Deep Belief Network. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0516.
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