基于卷积神经网络的细粒度情感分析方法
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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。
[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.
[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.
引文
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[15]李超.基于深度学习的短文本分类及信息抽取研究[D].郑州:郑州大学,2017.(Li Chao.Short Text Classification and Information Extraction Research Based on Deep Learning[D].Zhengzhou:Zhengzhou University,2017.)
[16]魏广顺,吴开超.基于词向量模型的情感分析[J].计算机系统应用,2017,26(3):182-186.(Wei Guangshun,Wu Kaichao.Sentiment Analysis Based on Word Vector Model[J].Computer Systems&Applications,2017,26(3):182-186.)
[17]Goldwater S,Griffiths T L.A Fully Bayesian Approach to Unsupervised Part-of-Speech Tagging[C]//Proceedings of the45th Annual Meeting of the Association of Computational Linguistics.2007:744-751.
[18]张志华.基于深度学习的情感词向量及文本情感分析的研究[D].上海:华东师范大学,2016.(Zhang Zhihua.Deep Learning Based Sentiment Word Vector Towards Sentiment Analysis[D].Shanghai:East China Normal University,2016.)
[19]Chen T,Xu R,He Y,et al.Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis[J].IEEE Computational Intelligence Magazine,2016,11(3):34-44.
[20]Hershey S,Chaudhuri S,Ellis D P W,et al.CNN Architectures for Large-Scale Audio Classification[C]//Proceedings of the2017 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2017:131-135.
[21]Bottou L.Stochastic Gradient Descent Tricks[J].Neural Networks:Tricks of the Trade,2012,7700:421-436.
[22]Srivastava N,Hinton G,Krizhevsky A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[23]陈钊.面向中文文本的情感分析方法研究[D].哈尔滨:哈尔滨工业大学,2015.(Chen Zhao.The Research on Sentiment Analysis on Chinese Text[D].Harbin:Harbin Institute of Technology,2015.)
(1)https://download.csdn.net/download/qq280929090/9818883.
[2]Pang B,Lee L,Vaithyanathan S.Thumbs up?:Sentiment Classification Using Machine Learning Techniques[C]//Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing.2002:79-86.
[3]Wu L,Hoi S C H,Yu N.Semantics-Preserving Bag-of-Words Models and Applications[J].IEEE Transactions on Image Processing,2010,19(7):1908-1920.
[4]Shen Y,He X,Gao J,et al.Learning Semantic Representations Using Convolutional Neural Networks for Web Search[C]//Proceedings of the 23rd International Conference on World Wide Web.2014:373-374.
[5]刘龙飞,杨亮,张绍武,等.基于卷积神经网络的微博情感倾向性分析[J].中文信息学报,2015,29(6):159-165.(Liu Longfei,Yang Liang,Zhang Shaowu,et al.Convolutional Neural Networks for Chinese Micro-blog Sentiment Analysis[J].Journal of Chinese Information Processing,2015,29(6):159-165.)
[6]苏小英,孟环建.基于神经网络的微博情感分析[J].计算机技术与发展,2015,25(12):161-164.(Su Xiaoying,Meng Huanjian.Sentiment Analysis of Micro-blog Based on Neural Networks[J].Computer Technology and Development,2015,25(12):161-164.)
[7]Kim Y.Convolutional Neural Networks for Sentence Classification[OL].Ar Xiv Preprint,ar Xiv:1408.5882.
[8]Irsoy O,Cardie C.Opinion Mining with Deep Recurrent Neural Networks[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.2014:720-728.
[9]Pilehvar M T,Collier N.Improved Semantic Representation for Domain-Specific Entities[C]//Proceedings of the 15th Workshop on Biomedical Natural Language Processing.2016:13-16.
[10]刘新星,姬东鸿,任亚峰.基于神经网络模型的产品属性情感分析[J].计算机应用,2017,37(6):1735-1740.(Liu Xinxing,Ji Donghong,Ren Yafeng.Product Property Sentiment Analysis Based on Neural Network Model[J].Journal of Computer Applications,2017,37(6):1735-1740.)
[11]Mikolov T,Chen K,Corrado G,et al.Efficient Estimation of Word Representations in Vector Space[OL].Ar Xiv Preprint.ar Xiv:1301.3781.
[12]孟园,王洪伟,王伟.网络口碑对产品销量的影响:基于细粒度的情感分析方法[J].管理评论,2017,29(1):144-154.(Meng Yuan,Wang Hongwei,Wang Wei.The Effect of Electronic Word-of-Mouth on Sales Through Fine-gained Sentiment Analysis[J].Management Review,2017,29(1):144-154.)
[13]卢兴.基于统计方法的中文短文本情感分析[D].北京:北京理工大学,2016.(Lu Xing.Sentiment Analysis of Chinese Short Text Based on Statistical Methods[D].Beijing:Beijing Institute of Technology,2016.)
[14]李慧,柴亚青.基于属性特征的评论文本情感极性量化分析[J].数据分析与知识发现,2017,1(10):1-11.(Li Hui,Chai Yaqing.Analyzing Sentiment Polarity of Comments Based on Attributes[J].Data Analysis and Knowledge Discovery,2017,1(10):1-11.)
[15]李超.基于深度学习的短文本分类及信息抽取研究[D].郑州:郑州大学,2017.(Li Chao.Short Text Classification and Information Extraction Research Based on Deep Learning[D].Zhengzhou:Zhengzhou University,2017.)
[16]魏广顺,吴开超.基于词向量模型的情感分析[J].计算机系统应用,2017,26(3):182-186.(Wei Guangshun,Wu Kaichao.Sentiment Analysis Based on Word Vector Model[J].Computer Systems&Applications,2017,26(3):182-186.)
[17]Goldwater S,Griffiths T L.A Fully Bayesian Approach to Unsupervised Part-of-Speech Tagging[C]//Proceedings of the45th Annual Meeting of the Association of Computational Linguistics.2007:744-751.
[18]张志华.基于深度学习的情感词向量及文本情感分析的研究[D].上海:华东师范大学,2016.(Zhang Zhihua.Deep Learning Based Sentiment Word Vector Towards Sentiment Analysis[D].Shanghai:East China Normal University,2016.)
[19]Chen T,Xu R,He Y,et al.Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis[J].IEEE Computational Intelligence Magazine,2016,11(3):34-44.
[20]Hershey S,Chaudhuri S,Ellis D P W,et al.CNN Architectures for Large-Scale Audio Classification[C]//Proceedings of the2017 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2017:131-135.
[21]Bottou L.Stochastic Gradient Descent Tricks[J].Neural Networks:Tricks of the Trade,2012,7700:421-436.
[22]Srivastava N,Hinton G,Krizhevsky A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[23]陈钊.面向中文文本的情感分析方法研究[D].哈尔滨:哈尔滨工业大学,2015.(Chen Zhao.The Research on Sentiment Analysis on Chinese Text[D].Harbin:Harbin Institute of Technology,2015.)
(1)https://download.csdn.net/download/qq280929090/9818883.