基于多样化特征卷积神经网络的情感分析
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摘要
深度网络模型在微博情感倾向性分析过程中难以有效利用情感特征信息,为此,提出一种基于多样化特征信息的卷积神经网络(MF-CNN)模型。结合词语多样化的抽象特征和2种网络输入矩阵计算方法,利用句中的情感信息,以优化情感分类效果。在COAE2014和微博语料数据集上进行文本情感分析,结果表明,MF-CNN模型的情感分类效果优于传统的分类器和深度卷积神经网络模型。
In the task of Micro-Blog sentiment analysis,the deep neural-based models are difficult to make full use of the sentiment information.To solve this problem,a Multiple Features Convolutional Neural Networks(MF-CNN) model is proposed.The emotional information in sentences is effectively utilized by combining the abstract features of words and two kinds of calculation methods of neural model input matrix,and then the sentiment classification result is optimized.The sentiment analysis is carried out on COAE2014 and Micro-Blog text data set,and the results show that the classification effect of MF-CNN model is better than that of traditional classifier and deep Convolutional Neural Network(CNN) model.
In the task of Micro-Blog sentiment analysis,the deep neural-based models are difficult to make full use of the sentiment information.To solve this problem,a Multiple Features Convolutional Neural Networks(MF-CNN) model is proposed.The emotional information in sentences is effectively utilized by combining the abstract features of words and two kinds of calculation methods of neural model input matrix,and then the sentiment classification result is optimized.The sentiment analysis is carried out on COAE2014 and Micro-Blog text data set,and the results show that the classification effect of MF-CNN model is better than that of traditional classifier and deep Convolutional Neural Network(CNN) model.
引文
[1] PANG B,LEE L.Opinion mining and sentiment analysis[J].Foundations and Trends in Information Retrieval,2008,2(1/2):1-135.
[2] HU M,LIU B.Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2004:168-177.
[3] 王仲远,程健鹏,王海勋,等.短文本理解研究[J].计算机研究与发展,2016,53(2):262-269.
[4] JOSHI A,BALAMURALI A R,BHATTACHARYYA P,et al.C-Feel-It:a sentiment analyzer for micro-blogs[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Tech-nologies:Systems Demonstrations.Stroudsburg,USA:Association for Computational Linguistics,2011:127-132.
[5] CHESLEY P,VINCENT B,XU L,et al.Using verbs and adjectives to automatically classify blog sentiment[J].Training,2006,580(263):233-235.
[6] BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Information Retrieval,2009,12(5):526-558.
[7] KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:Association for Computational Linguistics,2014:1746-1751.
[8] WANG X,LIU Y,SUN C,et al.Predicting polarities of Tweets by composing word embeddings with long short-term memory[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Stroudsburg,USA:Association for Computational Linguistics,2015:1343-1353.
[9] QIAN Q,HUANG M,ZHU X.Linguistically regularized LSTMs for sentiment classification[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Stroudsburg,USA:Association for Computa-tional Linguistics,2017:1679-1689.
[10] 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.
[11] 陈钊,徐睿峰,桂林,等.结合卷积神经网络和词语情感序列特征的中文情感分析[J].中文信息学报,2015,29(6):172-178.
[12] 何炎祥,孙松涛,牛菲菲,等.用于微博情感分析的一种情感语义增强的深度学习模型[J].计算机学报,2016,40(4):773-790.
[13] BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Information Retrieval,2009,12(5):526-558.
[14] 张志琳,宗成庆.基于多样化特征的中文微博情感分类方法研究[J].中文信息学报,2015,29(4):134-143.
[15] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 27th Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2013:3111-3119.
[16] COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing (almost) from scratch[J].Journal of Machine Learning Research,2011,12(8):2493-2537
[17] ZEILER M D.ADADELTA:an adaptive learning rate method[EB/OL].[2018-01-05].https://arxiv.org/pdf/1212.5701.pdf.
[2] HU M,LIU B.Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2004:168-177.
[3] 王仲远,程健鹏,王海勋,等.短文本理解研究[J].计算机研究与发展,2016,53(2):262-269.
[4] JOSHI A,BALAMURALI A R,BHATTACHARYYA P,et al.C-Feel-It:a sentiment analyzer for micro-blogs[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Tech-nologies:Systems Demonstrations.Stroudsburg,USA:Association for Computational Linguistics,2011:127-132.
[5] CHESLEY P,VINCENT B,XU L,et al.Using verbs and adjectives to automatically classify blog sentiment[J].Training,2006,580(263):233-235.
[6] BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Information Retrieval,2009,12(5):526-558.
[7] KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:Association for Computational Linguistics,2014:1746-1751.
[8] WANG X,LIU Y,SUN C,et al.Predicting polarities of Tweets by composing word embeddings with long short-term memory[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.Stroudsburg,USA:Association for Computational Linguistics,2015:1343-1353.
[9] QIAN Q,HUANG M,ZHU X.Linguistically regularized LSTMs for sentiment classification[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Stroudsburg,USA:Association for Computa-tional Linguistics,2017:1679-1689.
[10] 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.
[11] 陈钊,徐睿峰,桂林,等.结合卷积神经网络和词语情感序列特征的中文情感分析[J].中文信息学报,2015,29(6):172-178.
[12] 何炎祥,孙松涛,牛菲菲,等.用于微博情感分析的一种情感语义增强的深度学习模型[J].计算机学报,2016,40(4):773-790.
[13] BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Information Retrieval,2009,12(5):526-558.
[14] 张志琳,宗成庆.基于多样化特征的中文微博情感分类方法研究[J].中文信息学报,2015,29(4):134-143.
[15] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 27th Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2013:3111-3119.
[16] COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing (almost) from scratch[J].Journal of Machine Learning Research,2011,12(8):2493-2537
[17] ZEILER M D.ADADELTA:an adaptive learning rate method[EB/OL].[2018-01-05].https://arxiv.org/pdf/1212.5701.pdf.