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Sentiment Analysis with Reviewer Types and Generative Adversarial Network |
Li Feifei1,Wu Fan2,Wang Zhongqing2() |
1Library of Soochow University, Suzhou 215006, China 2School of Computer Science and Technology, Soochow University, Suzhou 215006, China |
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Abstract [Objective] This paper analyzes online comments by professional critics and average audience, aiming to improve the sentiment classification of reviews. [Methods] First, we introduced the professional backgrounds of contributors to examine the emotional polarity of reviews. Then, we used the generative adversarial network to decide whether the contributor was a professional critic or an average browser. Finally, we identified their differences to further improve the accuracy of emotion classification. [Results] The accuracy rate of the proposed model reached 83.6%, which was 5.6% higher than the benchmark model LSTM and 4.4% higher than BiLSTM. [Limitations] We only studied movie reviews, and more research is needed to evaluate our model with data sets from other fields. [Conclusions] The proposed GJOINT model can effectively improve the results of sentiment classification of online reviews.
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Received: 02 November 2020
Published: 17 May 2021
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Fund:National Natural Science Foundation of China(61806137);National Natural Science Foundation of China(61702351) |
Corresponding Authors:
Wang Zhongqing
E-mail: wangzq@suda.edu.cn
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[1] |
Wen X Z, Shao L, Xue Y, et al. A Rapid Learning Algorithm for Vehicle Classification[J]. Information Sciences, 2015,295(C):395-406.
doi: 10.1016/j.ins.2014.10.040
|
[2] |
Chen B J, Shu H Z, Coatrieux G, et al. Color Image Analysis by Quaternion-Type Moments[J]. Journal of Mathematical Imaging and Vision, 2015,51(1):124-144.
doi: 10.1007/s10851-014-0511-6
|
[3] |
Gu B, Sheng V S, Wang Z J, et al. Incremental Learning for ν-Support Vector Regression[J]. Neural Networks, 2015,67:140-150.
doi: 10.1016/j.neunet.2015.03.013
|
[4] |
李慧, 柴亚青. 基于卷积神经网络的细粒度情感分析方法[J]. 数据分析与知识发现, 2019,3(1):95-103.
|
[4] |
( Li Hui, Chai Yaqing. Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. Data Analysis and Knowledge Discovery, 2019,3(1):95-103.)
|
[5] |
Tang D Y, Qin B, Liu T. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 1422-1432.
|
[6] |
Wang S D, Manning C D. Baselines and Bigrams: Simple, Good Sentiment and Topic Classification[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. 2012: 90-94.
|
[7] |
Song G, Ye Y M, Du X L, et al. Short Text Classification: A Survey[J]. Journal of Multimedia, 2014,9(5):635-643.
|
[8] |
曾子明, 杨倩雯. 基于LDA和AdaBoost多特征组合的微博情感分析[J]. 数据分析与知识发现, 2018,2(8):51-59.
|
[8] |
( Zeng Ziming, Yang Qianwen. Sentiment Analysis for Micro-blogs with LDA and AdaBoost[J]. Data Analysis and Knowledge Discovery, 2018,2(8):51-59.)
|
[9] |
Hu F, Li L, Zhang Z L, et al. Emphasizing Essential Words for Sentiment Classification Based on Recurrent Neural Networks[J]. Journal of Computer Science and Technology, 2017,32(4):785-795.
doi: 10.1007/s11390-017-1759-2
|
[10] |
赵妍妍, 秦兵, 刘挺. 文本情感分析[J]. 软件学报, 2010,21(8):1834-1848.
|
[10] |
( Zhao Yanyan, Qin Bing, Liu Ting. Sentiment Analysis[J]. Journal of Software, 2010,21(8):1834-1848.)
|
[11] |
Tai K S, Socher R, Manning C D. Improved Semantic Representations from Tree-Structured Long Short-Term Memory Networks[OL]. arXiv Preprint, arXiv:1503.00075v2.
|
[12] |
吴鹏, 应杨, 沈思. 基于双向长短期记忆模型的网民负面情感分类研究[J]. 情报学报, 2018,37(8):845-853.
|
[12] |
( Wu Peng, Ying Yang, Shen Si. Negative Emotions of Online Users’ Analysis Based on Bidirectional Long Short-Term Memory[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(8):845-853.)
|
[13] |
Li X, Bing L D, Lam W, et al. Transformation Networks for Target-Oriented Sentiment Classification[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 946-956.
|
[14] |
卢强, 朱振方, 徐富永, 等. 融合语法规则的Bi-LSTM中文情感分类方法研究[J]. 数据分析与知识发现, 2019,3(11):99-107.
|
[14] |
( Lu Qiang, Zhu Zhenfang, Xu Fuyong, et al. Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules[J]. Data Analysis and Knowledge Discovery, 2019,3(11):99-107.)
|
[15] |
Hovy D. Demographic Factors Improve Classification Performance[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing. 2015: 752-762.
|
[16] |
Vosoughi S, Zhou H, Roy D, et al. Enhanced Twitter Sentiment Classification Using Contextual Information[C]// Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2015: 16-24.
|
[17] |
赵冬梅, 李雅, 陶建华, 等. 基于协同过滤Attention机制的情感分析模型[J]. 中文信息学报, 2018,32(8):128-134.
|
[17] |
( Zhao Dongmei, Li Ya, Tao Jianhua, et al. Sentiment Analysis Based on Collaborative Filter Attention Mechanism[J]. Journal of Chinese Information Processing, 2018,32(8):128-134.)
|
[18] |
Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997,9(8):1735-1780.
pmid: 9377276
|
[19] |
Wang K, Wan X J. SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018: 4446-4452.
|
[20] |
William F, Ian G, Andrew M D, et al. MaskGAN: Better Text Generation via Filling in the[C]// Proceedings of the 6th International Conference on Learning Representations. 2018: 1-17.
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