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Fine-grained emotion classification of Chinese microblogs based on graph convolution networks

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Abstract

Microblogs are widely used to express people’s opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse grammatical structures of Chinese microblogs. In addition, a pooling method based on percentile is proposed to improve the accuracy of the model. In experiments, for Chinese microblogs emotion classification categories including happiness, sadness, like, anger, disgust, fear, and surprise, the F-measure of our model reaches 82.32% and exceeds the state-of-the-art algorithm by 5.90%. The experimental results show that our model can effectively utilize the information of dependency parsing to improve the performance of emotion detection. What is more, we annotate a new dataset for Chinese emotion classification, which is open to other researchers.

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Notes

  1. https://github.com/zhanglinfeng1997/Sentiment-Analysis-via-GCN

  2. https://github.com/fxsjy/jieba

  3. http://ltp.ai

  4. http://tcci.ccf.org.cn/conference/2013/dldoc/evdata02.zip

  5. http://tcci.ccf.org.cn/conference/2013/dldoc/ev02.pdf

References

  1. A users number report of Sina Weibo: http://tech.sina.com.cn/i/2018-08-08/doc-ihhkuskt9903395.shtml. Accessed 27 Jan 2019

  2. Abdul-Mageed, M., Ungar, L.: Emonet: Fine-grained emotion detection with gated recurrent neural networks. ACL’17 1, 718–728 (2017)

    Google Scholar 

  3. Baziotis, C., Pelekis, N., Doulkeridis, C.: Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. SemEval’17, pp. 747–75 (2017)

  4. Boureau, Y., Bach, F., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp 2559–2566 (2010)

  5. Chen, S., Ding, Y., Xie, Z., Liu, S., Ding, H.: Chinese Weibo sentiment analysis based on character embedding with dual-channel convolutional neural network. ICCCBDA’18, pp. 107–111 (2018)

  6. He, Y., Sun, S., Niu, F., Li, F.: A deep learning model enhanced with emotion semantics for microblog sentiment analysis. Chin. J. Comput. 40(4), 773–790 (2017)

    Google Scholar 

  7. He, H., Xia, R.: Joint binary neural network for multilabel learning with applications to emotion classification. NLPCC’18 (2018)

  8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neur. Comput. 9 (8), 1735–1780 (1997)

    Article  Google Scholar 

  9. Jiang, F., Liu, Y., Luan, H., Sun, J., Zhu, X., Zhang, M., Ma, S.: Microblog sentiment analysis with emoticon space model. J. Comput. Sci. Technol. 30 (5), 1120–1129 (2015)

    Article  Google Scholar 

  10. Jianqiang, Z., Xiaolin, G., Xuejun, Z.: Deep convolution neural networks for twitter sentiment analysis. IEEE Access. 6, 23253–23260 (2018)

    Article  Google Scholar 

  11. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences (CVPR). ACL’14 2014, 655–665 (2014)

    Google Scholar 

  12. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. ACL’14, pp. 655–665 (2014)

  13. Kim, Y.: Convolutional neural networks for sentence classification. EMNLP’14, pp. 1746–1751 (2014)

  14. Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5Th International Conference on Learning Representations(ICLR) (2016)

  15. Koo, T., Carreras, X., Collins, M.: Simple semi-supervised dependency parsing. ACL’08, pp. 595–603 (2008)

  16. Lécun, Y, Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  17. Lee, J.Y., Dernoncourt, F.: Sequential Short-Text classification with recurrent and convolutional neural networks. NAACL’16, pp. 515–520 (2016)

  18. Lei, Z., Yang, Y., Yang, M., Liu, Y.: A multi-sentiment-resource enhanced attention network for sentiment classification. ACL’18, pp. 758–763 (2018)

  19. Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert. Systems. Appl. 41(4), 1742–1749 (2014)

    Article  Google Scholar 

  20. Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proc of AAAI (2018)

  21. Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling. EMNLP’17, pp. 1506–1515 (2017)

  22. Mcdonald, R., Pereira, F.: Online learning of approximate dependency parsing algorithms. EACL’06 (2006)

  23. Moorthy, A.K., Bovik, A.C.: Visual importance pooling for image quality assessment. IEEE J. Sel. Top. Signal. Process. 3(2), 193–201 (2009)

    Article  Google Scholar 

  24. Nguyen, T.H., Grishman, R.: Graph convolutional networks with Argument-Aware pooling for event detection. AAAI’18, pp. 5900–5907 (2018)

  25. Qian, Q., Huang, M., Lei, J., Zhu, X.: Linguistically regularized SLTMs for sentiment classification. ACl’16, pp. 1679–1689 (2016)

  26. Rosenthal, S., Farra, N., Nakov, P.: Semeval2017 task 4: Sentiment analysis in Twitter. SemEval’17, pp. 502–518 (2017)

  27. Saad, M., Bovik, A., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image. Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  28. Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. ACL’13, pp. 1631–1642 (2013)

  29. Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. EMNLP’15, pp. 1422–1432 (2015)

  30. Wang, J., Yu, L., Lai, K., Zhang, X.: Dimensional sentiment analysis using a regional CNN-LSTM model. ACL’16 2, 225–230 (2016)

    Google Scholar 

  31. Wang, Y., Feng, S., Wang, D., Yu, G., Zhang, Y.: Multi-label Chinese microblog emotion classification via convolutional neural network. APWeb’16, pp. 567–580 (2016)

  32. Wen, S., Wan, X.: Emotion classification in microblog texts using class sequential rules. AAAI’14, pp. 187–193 (2014)

  33. Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. ACL’18, pp 2514–2523 (2018)

  34. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. AAAI’18, pp. 7444–7452 (2018)

  35. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1098–1105 (2012)

  36. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W., Leskovec, J.: Graph convolutional neural networks for Web-Scale recommender systems. KDD’18, pp. 974–983 (2018)

  37. Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. AAAI’18, pp. 3634–3640 (2018)

  38. Yuan, Z., Purver, M.: Predicting emotion labels for chinese microblog texts. Adv. Soc. Media. Analy., pp. 129–149 (2015)

  39. Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. ACL’17 1, 253–263 (2017)

    Google Scholar 

  40. Zhao, J., Liu, K., Xu, L.: Sentiment analysis: Mining opinions, sentiments, and emotions. Comput. Linguis. 42(3), 595–598 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Key R&D Program of China (No. 20-16YFC1401900), the National Natural Science Foundation of China (61173029, 61672144, 61872072), and the Australian Research Council Discovery Grants (DP170104747, DP180100212).

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    Authors and Affiliations

    1. Northeastern University, Shenyang, 110819, China

      Yuni Lai, Linfeng Zhang & Donghong Han

    2. Swinburne University of Technology, Hawthorn, 3122, Australia

      Rui Zhou

    3. Beijing Institute of Technology, Beijing, 100081, China

      Guoren Wang

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    1. Yuni Lai
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    2. Linfeng Zhang
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    3. Donghong Han
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    4. Rui Zhou
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    Correspondence to Donghong Han.

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    Yuni Lai and Linfeng Zhang contributed equally to this paper.

    This article belongs to the Topical Collection: Special Issue on Application-Driven Knowledge Acquisition

    Guest Editors: Xue Li, Sen Wang, and Bohan Li

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    Lai, Y., Zhang, L., Han, D. et al. Fine-grained emotion classification of Chinese microblogs based on graph convolution networks. World Wide Web 23, 2771–2787 (2020). https://doi.org/10.1007/s11280-020-00803-0

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