Sentiment Analysis Based on Collaborative Filter Attention Mechanism
ZHAO Dongmei 1,2, LI Ya2, TAO Jianhua 2, GU Mingliang1
1.School of Physics Electronic Engineering, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China; 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100101, China
Abstract:This paper investigates the influence of users personality and product information on data emotion category in review data. Among the many factors that affect the emotional data type, the subject of the evaluation, that is, the user and the object of the evaluation, are emotionally important to the commentary data. In this paper, an emotional analysis model (LSTM-CFA) based on cooperative filtering attention mechanism is proposed. The user interest distribution matrix is calculated by using the collaborative filtering (CF) algorithm. After the matrix is decomposed with SVD, the matrix is added to the hierarchical LSTM model as an attention mechanism in order to achieve emotion classification. Experiments show that the LSTM-CFA model can extract the information of users personality and product attribute efficiently, to improve the accuracy of emotion classification.
[1] 赵妍妍,秦兵,刘挺.文本情感分析[J]. 软件学报, 2010, 21(8): 1834-1848. [2] Seroussi Y, Zukerman I, Bohnert F. Collaborative Inference of Sentiments from Texts[C]//Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Springer-Verlag, 2010: 195-206. [3] Diao Q, Qiu M, Wu C Y, et al. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)[C]//Proceedings of the ACM Sigkdd International Conference on Knowledge Discovery & Data Mining. ACM, 2014: 193-202. [4] Hinton G E. Learning distributed representations of concepts[C]//Proceedings of the CogSci. Amherst, MA, 1986: 1-12. [5] Mikolov T, Chen K, Corrado G, et al. Efficient Estimation of Word Representations in Vector Space[J]. arXiv preprint arXiv. 2013: 1301.3781. [6] Socher R, Pennington J, Huang E H, et al. Semi-supervised recursive autoencoders for predicting sentiment distributions[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011: 27-31. [7] Socher R, Huval B, Manning C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012: 1201-1211. [8] Socher R, Perelygin A, Wu J Y, et al. Recursive deep models for semantic compositionality over a sentiment treebank[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. 2013: 1631-1642. [9] Kim Y. Convolutional Neural Networks for Sentence Classification[J]. arXiv preprint arXiv,2014: 1408.5882. [10] Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[J]. arXiv preprint arXiv,2014: 1404.2188. [11] Tai K S,Socher R, Manning C D. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks[J]. Computer Science, 2015: 5(1): 36. [12] Tang D, Qin B, Liu T. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2015: 1422-1432. [13] Yang Z, Yang D, Dyer C, et al. Hierarchical Attention Networks for Document Classification[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2017: 1480-1489. [14] Wenliang Gao, Naoki Yoshinaga, Nobuhiro Kaji, et al. Modeling user leniency and product popularity for sentiment classification[C]//Proceedings of the IJCNLP, 2013: 1107-1111. [15] Zhou C, Sun C, Liu Z, et al. A C-LSTM Neural Network for Text Classification[J]. Computer Science, 2015: 1(4): 39-44. [16] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997: 9(8): 1735-1780. [17] 鲁 骁,王书鑫,王 斌,鲁 凯. 一种融合地理位置信息的协同过滤推荐算法[J]. 中文信息学报, 2016,30(2): 64-73. [18] 张时俊,王永恒. 基于矩阵分解的个性化推荐系统研究[J]. 中文信息学报, 2017, 31(3): 134-139. [19] 来斯惟. 基于神经网络的词和文档语义向量表示方法研究[D]. 北京: 中国科学院大学博士学位论文, 2016. [20] Le Q V,Mikolov T. Distributed Representations of Sentences and Documents[C]//Proceedings of the International Conference on Machine Learning, 2014, 4(Vol.Ⅱ): 1188-1196. [21] Mikolov T, Sutskever I, Chen K, et al. Distributed Representations of Words and Phrases and their Compositionality[J]. Advances in Neural Information Processing Systems, 2013: 26: 3111-3119. [22] Tang D, Qin B, Liu T. Learning Semantic Representations of Users and Products for Document Level Sentiment Classification[C]//Proceedings of the Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing. 2015: 1014-1023. [23] Dou Z, et al. Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2017. [24] Long Y, Lu Q, Xiang R, et al. A Cognition Based Attention Model for Sentiment Analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2017.