目标级情感分类任务是为了得到句子中特定评价目标的情感倾向。一个句子中往往存在多个目标,多个目标的情感可能一致,也可能不一致。但在已有针对目标级情感分类的评测数据集中: ①大多数是一个句子一个目标; ②在少数有多个目标的句子中,多个目标情感倾向分布并不均衡,多个目标情感一致的句子占较大比例。数据集本身的缺陷限制了模型针对多个目标进行情感分类的提升空间。针对以上问题,该文构建了一个针对多目标情感分类的中文数据集,人工标注了6 339个评价目标,共2 071条数据。该数据集具备以下特点: ①评价目标个数分布平衡; ②情感正负极性分布平衡; ③多目标情感倾向分布平衡。随后,该文利用多个目标情感分类的主流模型在该数据集上进行了实验与比较分析。结果表明,现有主流模型尚不能对存在多个目标且目标情感倾向性不一致实例中的目标进行很好的分类,尤其是目标的情感倾向为中性时。因此多目标情感分类任务具有一定的难度与挑战性。
Abstract
Target-level sentiment classification task is to get the sentiment tendency of a specific evaluation target in a sentence. There are often multiple targets in a comment sentence, and the sentiments of multiple targets may be consistent or inconsistent. However, in the existing evaluation datasets for target-level sentiment classification: 1) most of them are single sentence with one target; 2) in a few sentences with multiple targets, the sentiment distribution of multiple targets is seriously biased: most multiple targets have the same emotion. In response to the above problems, this paper constructs a Chinese dataset for multi-target sentiment classification, totaling 2,071 items with 6,339 targets manually annotated. The data set provides balance distribution for the number of evaluation targets, positive and negative sentiments, and multi-target sentimental tendency. Meanwhile, this article uses multiple mainstream models of target-level sentiment classification to conduct experiments and comparative analysis on this dataset. Experimental results show that the existing mainstream models are still unable to well classify the targets in instances where there are multiple targets and the target's sentiment is inconsistent, especially when the target's sentiment is neutral.
关键词
目标级情感分类 /
中文数据集 /
多目标
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Key words
target-level sentiment classification /
Chinese dataset /
multi-target
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参考文献
[1] Li Dong,Furu Wei, Chuanqi Tan, 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 (Volume 2: Short Papers),2014: 49-54.
[2] Duy Tin Vo, Yue Zhang. Target-dependent Twitter sentiment classification with rich automatic features[C]//Proceedings of IJCAI,2015: 1347-1353.
[3] Duyu Tang, Bing Qin, Xiaocheng Feng, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016:3298-3307.
[4] Ruder S,Ghaffari P, Breslin J G. A hierarchical model of reviews for aspect-based sentiment analysis[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016: 999-1005.
[5] Cho K, van Merrinboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 1724-1734.
[6] Wang Y, Huang M, Zhu X, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016: 606-615.
[7] Dehong Ma, Sujian Li, Xiaodong Zhang, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of IJCAI-2017, 2017: 4068-4074.
[8] Chen P, Sun Z, Bing L, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 452-461.
[9] 曾锋, 曾碧卿, 韩旭丽, 等. 基于双层注意力循环神经网络的方面级情感分析[J]. 中文信息学报, 2019, 33(6): 108-115.
[10] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, 2019: 4171-4186.
[11] Song Y, Wang J, Jiang T, et al. Attentional encoder network for targeted sentiment classification[J]. arXiv preprint arXiv:1902.09314, 2019.
[12] 杜成玉,刘鹏远. 基于螺旋注意力网络的方面级别情感分析模型[J]. 中文信息学报, 2020, 34(9): 70-77.
[13] M Pontiki, D Galanis, J Pavlopoulos, et al. SemEval-2014 task 4: Aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation (Sem Eval 2014), Dublin, Ireland, 2014: 27-35.
[14] Long Jiang, Mo Yu, Ming Zhou, et al. Target-dependent Twitter sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 2011: 151-160.
[15] Xiaowen Ding, Bing Liu, Philip S Yu. A holistic lexicon-based approach to opinion mining[C]//Proceedings of the 2008 International Conference on Web Search and Data Mining, 2008: 231-240.
[16] Wayne Xin Zhao, Jing Jiang,Hongfei Yan, et al. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 2010: 56-65.
[17] Duyu Tang, Bing Qin, et al. Aspect level sentiment classification with deep memory network[C]//Proceedings of EMNLP,2016: 214-224.
[18] Li L, Liu Y, Zhou A Q. Hierarchical attention based position-aware network for aspect-level sentiment analysis[C]//Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018: 181-189.
[19] Huang B, Carley K. Parameterized convolutional neural networks for aspect level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018: 1091-1096.
[20] Wei Xue, Tao Li. Aspect based sentiment analysis with gated convolutional networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018: 2514-2523.
[21] Pinlong Zhao, Linlin Hou, Ou Wu. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification[J]. Knowlege-Based Systems, 2020, 193: 105443.
[22] Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, et al. SemEval-2015 task 12: Aspect based sentiment analysis[C]//Proceedings of the 9th International Workshop on Semantic Evaluation, 2015: 486-495.
[23] Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, et al. SemEval-2016 task 5: Aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic Evaluation,2016:19-30.
[24] Ganu G, Elhadad N, Marian A. Beyond the stars: Improving rating predictions using review text content[C]//Proceedings of the 12th International Workshop on the Web and Databases. 2009, 9: 1-6.
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基金
北京市自然科学基金(4192057);教育部人文社会科学研究规划基金(18YJA740030);北京语言大学校级项目(中央高校基本科研业务费专项资金)(17PT05)
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