Aspect Embedding on Memory Network for Aspect Sentiment Classification
LIU Yiyi1,2, ZHANG Jin2, YU Zhihua2, LIU Yue2, CHENG Xueqi3
1.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049; 2.Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190; 3.Institute of Network Technology, Institute of Computing Technology(YANTAI), Chinese Academy of Sciences, Yantai264005
Abstract：A comment contains multiple aspects and sentiments, and therefore it is difficult to classify sentiment polarity of different aspects correctly. A model combining aspect embedding with memory neural network is proposed to identify the sentiment of aspects in a comment. The aspect word vector is introduced into different modules of the memory network. The semantic information of the word is reinforced. The attention mechanism is guided to capture the relevant context, and thus the sentiment classification effect in the aspect is improved. Experimental results on short text English comments of SemEval 2014 Task 4 dataset and the long-text Chinese news dataset indicate that the proposed method achieves good classification effect and fully verifies the validity of the word embedding information introduced into the framework of the memory network.
[1] ZHANG L, LIU B. Sentiment Analysis and Opinion Mining // SAMMUT C, WEBB G, eds. Encyclopedia of Machine Learning and Data Mining. Berlin, Germany: Springer, 2016: 1-167. [2] DING X W, LIU B, YU P S. A Holistic Lexicon-Based Approach to Opinion Mining // Proc of the International Conference on Web Search and Data Mining. New York, USA: ACM, 2008: 231-240. [3] GANAPATHIBHOTLA M, LIU B. Mining Opinions in Comparative Sentences // Proc of the 22nd International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 2008, I: 241-248. [4] JIANG L, YU M, ZHOU M, et al. Target-Dependent Twitter Sentiment Classification // Proc of the 49th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2011, II: 151-160. [5] ZHAO W X, JIANG J, YAN H F, et al. Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2010: 56-65. [6] DONG L, WEI F R, TAN C Q, et al. Adaptive Recursive Neural Network for Target-Dependent Twitter Sentiment Classification // Proc of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2014, II: 49-54. [7] TANG D Y, QIN B, FENG X C, et al. Target-Dependent Sentiment Classification with Long Short Term Memory[C/OL]. [2019-09-25]. https://arxiv.org/pdf/1512.01100v1.pdf. [8] HOCHREITER S, SCHMIDHUBER J. Long Short-Term Memory. Neural Computation, 1997, 9(8): 1735-1780. [9] VASWANI A, SHAZEER N, PARMAR N, et al. Attention Is All You Need // GUYON I, LUXBURG U V, BENGIO S, et al., eds. Advances in Neural Information Processing Systems 30. Cambridge, USA: The MIT Press, 2017: 5998-6008. [10] WANG Y Q, HUANG M L, ZHAO L. Attention-Based LSTM for Aspect-Level Sentiment Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2016: 606-615. [11] SUKHBAATAR S, SZLAM A, WESTON J, et al. End-to-End Memory Networks // CORTE S, LAWRENCE N D, LEE D D, et al., eds. Advances in Neural Information Processing Systems 28. Cambridge, USA: The MIT Press, 2015: 2440-2448. [12] TANG D Y, QIN B, LIU T. Aspect Level Sentiment Classification with Deep Memory Network // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2016: 214-224. [13] CHEN P, SUN Z Q, BING L D, et al. Recurrent Attention Network on Memory for Aspect Sentiment Analysis // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2017: 452-461. [14] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[C/OL]. [2019-09-25]. https://arxiv.org/pdf/1412.3555.pdf. [15] PENNINGTON J, SOCHER R, MANNING C D. GloVe: Global Vectors for Word Representation // Proc of the Conference on Em- pirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1532-1543. [16] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space [C/OL]. [2019-09-25]. https://arxiv.org/pdf/1301.3781.pdf. [17] KIRITCHENKO S, ZHU X D, CHERRY C, et al. NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews // Proc of the 8th International Workshop on Semantic Evaluation. Stroudsburg, USA: ACL, 2014: 437-442.
2019, Vol. 32 
