一种用于特定目标情感分析的深度网络模型
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摘要
基于注意力机制的长短期记忆(LSTM)网络在训练过程中需要耗费大量时间,且仅以句子作为网络输入难以有效区分同一句中不同目标的情感极性。为此,提出一种结合卷积神经网络(CNN)和区域LSTM的深度网络模型。通过区域LSTM实现特定目标的区域划分,在保留特定目标重要情感信息的同时,有效区分不同目标的特征信息,并利用CNN保留整个句子的情感信息。实验结果表明,该模型能有效识别不同目标的情感极性,相比传统网络模型具有更短的模型训练时间。
The Long Short Term Memory(LSTM) network based on attention mechanism generally takes a lot of time during the training process,and only uses sentences as a network input,which is difficult to effectively distinguish the different polarities of different targets in the same sentence.To address this problem,this paper proposes a deep neural model of combining Convolutional Neural Network(CNN) and Regional LSTM(CNN-RLSTM).By segmenting the region according to the specific target through the regional LSTM,the feature information of different targets can be effectively distinguished while retaining the specific emotional information of the specific target,and the emotional information of the entire sentence is retained by the CNN.Experimental results show that, the CNN-RLSTM model can effectively identify the emotional polarity of different targets,and the model training time is shorter than the traditional network model.
The Long Short Term Memory(LSTM) network based on attention mechanism generally takes a lot of time during the training process,and only uses sentences as a network input,which is difficult to effectively distinguish the different polarities of different targets in the same sentence.To address this problem,this paper proposes a deep neural model of combining Convolutional Neural Network(CNN) and Regional LSTM(CNN-RLSTM).By segmenting the region according to the specific target through the regional LSTM,the feature information of different targets can be effectively distinguished while retaining the specific emotional information of the specific target,and the emotional information of the entire sentence is retained by the CNN.Experimental results show that, the CNN-RLSTM model can effectively identify the emotional polarity of different targets,and the model training time is shorter than the traditional network model.
引文
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[2] 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.
[3] PONTIKI M,GALANIS D,PAVLOPOULOS J,et al.SemEval-2014 task 4:aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation.Stroudsburg,USA:ACL Press,2014:27-35.
[4] BOIY E,MOENS M F.A machine learning approach to sentiment analysis in multilingual Web texts[J].Information Retrieval,2009,12(5):526-558.
[5] 余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.
[6] 王盛玉,曾碧卿,胡翩翩.基于卷积神经网络参数优化的中文情感分析[J].计算机工程,2017,43(8):200-207,214.
[7] KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:ACL Press,2014:1746-1751.
[8] ZHOU P,SHI W,TIAN J,et al.Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Stroudsburg,USA:ACL Press,2016:207-212.
[9] TANG D,QIN B,LIU T.Aspect level sentiment classification with deep memory network[C]//Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:ACL Press,2016:214-224.
[10] 梁斌,刘全,徐进,等.基于多注意力卷积神经网络的特定目标情感分析[J].计算机研究与发展,2017,54(8):1724-1735.
[11] 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,2004:168-177.
[12] HU M,LIU B.Mining opinion features in customer reviews[C]//Proceedings of the 19th National Conference on Artifical Intelligence.Palo Alto,USA:AAAI Press,2004:755-760.
[13] KIRITCHENKO S,ZHU X,CHERRY C,et al.NRC-Canada-2014:detecting aspects and sentiment in customer reviews[C]//Proceedings of the 8th International Workshop on Semantic Evaluation.Stroudsburg,USA:ACL Press,2014:437-442.
[14] NGUYEN T H,SHIRAI K.PhraseRNN:phrase recursive neural network for aspect-based sentiment analysis[C]//Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:ACL Press,2015:2509-2514.
[15] DONG L,WEI F,TAN C,et al.Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.Stroudsburg,USA:ACL Press,2014:49-54.
[16] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[EB/OL].[2017-11-15].https://arxiv.org/abs/1409.0473.
[17] YIN W,SCHüTZE H,XIANG B,et al.ABCNN:attention-based convolutional neural network for modeling sentence pairs[J].Transactions of the Association for Computational Linguistics,2016,4(11):259-272.
[18] WANG Y,HUANG M,ZHAO L,et al.Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of 2016 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:ACL Press,2016:606-615.
[19] COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing(almost) from scratch[J].Journal of Machine Learning Research,2011,12:2493-2537.
[20] PENNINGTON J,SOCHER R,MANNING C D.GloVe:Global Vectors for Word Representation[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing.Stroudsburg,USA:ACL Press,2014:1532-1543.
[21] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[EB/OL].[2017-11-15].https://arxiv.org/abs/1301.3781.
[22] 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:ACL Press,2015:1343-1353.