Abstract Targets are usually discussed together. Sentiment towards the given target may be different from the sentiment polarity of the whole text. It is necessary to focus on the related context to the target in the whole semantic scenario for targeted sentiment analysis tasks. This paper presents a targeted sentiment classification method based on convolutional neural network(CNN) with Part-of-Speech(POS) and attention mechanism. POS information is introduced into the model as a supplement to text features. Attention mechanism with respect to the given target is built based on long short term memory neural network(LSTM) modeling of the input sequence. Then, the relevant parts to the target of the input text are enhanced according to the attention and the modified sequence is input to CNN sentiment classification structure to analyze the polarity towards the given target. POS information helps to capture the context with collocation relation to the target, which will help to reduce the influence of the context with similar content or short distance but no collocation relation. LSTM and CNN modeling the input text together can be beneficial to capture semantics of the whole text and those towards the given target at the same time effectively. Experiments on SemEval2014 dataset shows the effectiveness of the model compared to attention methods based on LSTM.
Fund:Supported by Science and Technology Plan Project of Xizang(Tibet) Autonomous Region(No.XZ201801-GB-17)
About author:: (DU Hui, Ph.D., assistant professor. Her research interests include sentiment analysis and machine learning.) (YU Xiaoming, Ph.D., associate professor. His research interests include information retrieval and big data.) (LIU Yue, Ph.D., associate professor. Her research interests include information retrieval and big data.) (YU Zhihua, Ph.D., senior engineer. His research interests include network information processing and information security.) (CHENG Xueqi, Ph.D., professor. His research interests include bid data analysis and mining.)
[1] 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, I: 151-160. [2] 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, I: 49-54. [3] WANG B, LIAKATA M, ZUBIAGA A, et al. TDParse: Multi-target-specific Sentiment Recognition on Twitter // Proc of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2017, I: 483-493. [4] ZHANG M S, ZHANG Y, VO D T. Gated Neural Networks for Targeted Sentiment Analysis // Proc of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2016: 3087-3093. [5] 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. [6] WANG Y Q, HUANG M L, ZHAO L, 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. [7] YANG M, TU W T, WANG J X, et al. Attention-Based LSTM for Target Dependent Sentiment Classification // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 5013-5014. [8] LIU J M, ZHANG Y. Attention Modeling for Targeted Sentiment // Proc of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg,USA: ACL, 2017, II: 572-577. [9] WANG X B, CHEN G. Dependency-Attention-Based LSTM for Target-Dependent Sentiment Analysis // Proc of the 6th Chinese National Conference on Social Media Processing. Berlin, Germany: Springer, 2017: 206-217. [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.计算机研究与发展, 2017, 54(8): 1724-1735. (LIANG B, LIU Q, XU J, et al. Aspect-Based Sentiment Analysis Based on Multi-attention CNN. Journal of Computer Research and Development, 2017, 54(8): 1724-1735.) [11] PONTIKI M, GALANIS D, PAVLOPOULOS 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. [12] PENNINGTON J, SOCHER R, MANNING C. GloVe: Global Vectors for Word Representation // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1532-1543. [13] TOUTANOVA K, KLEIN D, MANNING C D, et al. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network // Proc of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Stroudsburg, USA: ACL, 2003: 252-259. [14] KIM Y. Convolutional Neural Networks for Sentence Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1746-1751. [15] 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. Stroudsburg, USA: ACL, 2016: 3298-3307.
2018, Vol. 31 
