Aspect Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network
DENG Liming1,2,3, WEI Jingjing4, WU Yunbing1,2,3, YU Xiaoyan1,2,3, LIAO Xiangwen1,2,3
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116 3. Digital Fujian Institute of Financial Big Data, Fuzhou 350116 4. College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou 350108
Abstract:The existing aspect level sentiment analysis methods cannot solve the problem of polysemous word in different contexts. Therefore, a method for aspect level sentiment analysis based on knowledge graph and recurrent attention network is proposed. The text representation of the bidirectional long short-term memory network is integrated with synonym information in knowledge graph using dynamic attention mechanism to obtain the state vector of knowledge perception. To classify aspect level sentiment, the memory content is constructed by combining the location information and inputting the multi-level gated recurrent unit for calculating the sentiment characteristics of aspect terms. The experimental results show that the proposed method achieves better classification results on three open datasets.
[1] ELGAMAL M. Sentiment Analysis Methodology of Twitter Data with an Application on HAJJ Season. International Journal of Engineering Research and Science, 2016, 2(1): 82-87. [2] PONTIKI M, GALANIS D, PAYLOPOULOS J, et al. SemEval-2014 Task 4: Aspect Based Sentiment Analysis // Proc of the 8th International Workshop on Semantic Evaluation. Stroudsburg, USA: ACL, 2014: 27-35. [3] LIU B. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167. [4] MOGHADDAM S, ESTER M. Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews // Proc of the 19th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2010: 1825-1828. [5] 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. [6] WAGNER J, ARORA P, CORTES S, et al. DCU: Aspect-Based Polarity Classification for SemEval Task 4 // Proc of the 8th International Workshop on Semantic Evaluation. Stroudsburg, USA: ACL, 2014. 223-229. [7] LIU Q, ZHANG H B, ZENG Y F, et al. Content Attention Model for Aspect Based Sentiment Analysis // Proc of the World Wide Web Conference. Washington, USA: IEEE, 2018: 1023-1032. [8] 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 Computatio-nal Linguistics(Short Papers). Stroudsburg, USA: ACL, 2014: 49-54. [9] TANG D Y, QIN B, FENG X C, et al. Effective LSTMs for Target-Dependent Sentiment Classification // Proc of the 26th International Conference on Computational Linguistics(Technical Papers). Stroudsburg, USA: ACL, 3298-3307. [10] HUANG B X, CARLEY K M. Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2018: 1091-1096. [11] LEI Z Y, YANG Y J, YANG M, et al. A Human-Like Semantic Cognition Network for Aspect-Level Sentiment Classification // Proc of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2019: 6650-6657. [12] WANG Y Q, HUANG M L, ZHU X Y, et al. 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. [13] 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. [14] MA D H, LI S J, ZHANG X D, et al. Interactive Attention Networks for Aspect-Level Sentiment Classification // Proc of the 26th International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2017: 4068-4074. [15] 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. [16] WANG B L, LU W. Learning Latent Opinions for Aspect-Level Sentiment Classification // Proc of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2018: 5537-5544. [17] KARPATHY A, JOHNSON J, LI F F. Visualizing and Understanding Recurrent Networks[C/OL]. [2020-02-26]. https://arxiv.org/pdf/1506.02078.pdf. [18] LIN Y K, LIU Z Y, SUN M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 2181-2187. [19] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating Embeddings for Modeling Multi-relational Data // BURGES C J C, BOTTOU L, WELLING M, et al., eds. Advances in Neural Information Processing Systems 26. Cambridge, USA: The MIT Press, 2013: 2787-2795. [20] WANG X Y, XU G L, ZHANG J Y, et al. Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis. IEEE Access, 2019, 7: 5014-5025. [21] WANG Z, ZHANG J W, FENG J L, et al. Knowledge Graph Embedding by Translating on Hyperplanes // Proc of the 28th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2014: 1112-1119.