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Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks

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Abstract

In microblog sentiment analysis, the similarity between microblogs and social attributes between users are often used, but previous work usually contains a large number of disruptive microblogs due to the consideration of the relationships between the target microblog user and many other users. We attempt to explore this problem from the perspective of user representative relationship and propose the multi-interaction hybrid neural networks that consist of graph attention networks, convolutional neural network, and bidirectional long short-term memory network with bidirectional gated recurrent unit and attention. Unlike existing methods for finding similar microblog sequences or adding user node embeddings, our method focuses on using the most similar user to the target microblog user rather than all similar users, which can avoid the interference of invalid microblogs of similar users. The experimental results on four real-world microblog datasets demonstrate the superiority of our method compared with nine state-of-the-art methods.

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Notes

  1. https://pypi.org/project/jieba/.

  2. https://www.nltk.org/api/nltk.corpus.html.

  3. https://github.com/goto456/stopwords/blob/master/cn_stopwords.txt.

  4. https://www.nltk.org/.

  5. http://goo.gl/iXzoXm.

  6. http://archive.ics.uci.edu/ml/datasets/microblogpcu.

  7. https://github.com/baidu/Senta.

  8. https://www.paddlepaddle.org.cn/hublist.

References

  1. Abelson, R.P.: Whatever became of consistency theory? Personal. Soc. Psychol. Bull. 9(1), 3754 (1983)

    Article  Google Scholar 

  2. Bharti, S.K., Vachha, B., Pradhan, R., Babu, K.S., Jena, S.K.: Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digit. Commun. Netw. 2(3), 108121 (2016)

    Google Scholar 

  3. Dalaorao, G.A., Sison, A.M., Medina, R.P.: Integrating collocation as tf-idf enhancement to improve classification accuracy. In: 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA), pp. 282-285. IEEE (2019)

  4. Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. Mach. Learn. 37(3), 277296 (1999)

    Article  MATH  Google Scholar 

  5. Gan, C., Feng, Q., Zhang, Z.: Scalable multi-channel dilated cnnbilstm model with attention mechanism for Chinese textual sentiment analysis. Future Gener. Comput. Syst. 118, 297309 (2021)

    Article  Google Scholar 

  6. Ghiassi, M., Lee, S.: A domain transferable lexicon set for twitter sentiment analysis using a supervised machine learning approach. Expert Syst. Appl. 106, 197216 (2018)

    Article  Google Scholar 

  7. Gupta, P., Kumar, S., Suman, R., Kumar, V.: Sentiment analysis of lockdown in India during COVID-19: a case study on twitter. IEEE Trans. Comput. Soc. Syst. 8(4), 9921002 (2020)

    Google Scholar 

  8. Hatfield, E., Cacioppo, J.T., Rapson, R.L.: Emotional contagion. Curr. Dir. Psychol. Sci. 2(3), 96100 (1993)

    Article  Google Scholar 

  9. Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 537–546 (2013)

  10. Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014)

  11. Januário, B.A., Carosia, A.E.D.O., da Silva, A.E.A., Coelho, G.P.: Sentiment analysis applied to news from the Brazilian stock market. IEEE Latin Am. Trans. 20(3), 512–518 (2021)

    Article  Google Scholar 

  12. Ji, R., Chen, F., Cao, L., Gao, Y.: Cross-modality microblog sentiment prediction via bi-layer multimodal hypergraph learning. IEEE Trans. Multimed. 21(4), 10621075 (2018)

    Google Scholar 

  13. Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)

  14. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1167 (2012)

    Google Scholar 

  15. Lu, T.J.: Semi-supervised microblog sentiment analysis using social relation and text similarity. In: 2015 International Conference on Big Data and Smart Computing (BigComp), pp. 194–201. IEEE (2015)

  16. Mi, C., Ruan, X., Xiao, L.: Microblog sentiment analysis using user similarity and interaction-based social relations. Int. J. Web Serv. Res. (IJWSR) 17(3), 3955 (2020)

    Google Scholar 

  17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 31113119 (2013)

    Google Scholar 

  18. Naz, S., Sharan, A., Malik, N.: Sentiment classification on twitter data using support vector machine. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 676–679. IEEE (2018)

  19. Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

  20. Pozzi, F.A., Maccagnola, D., Fersini, E., Messina, E.: Enhance user-level sentiment analysis on microblogs with approval relations. In: Matteo, B., Cristina, B., Guido, B., Roberto, M. (eds.) Congress of the Italian Association for Artificial Intelligence, pp. 133–144. Springer (2013)

  21. Shamma, D.A., Kennedy, L., Churchill, E.F.: Tweet the debates: understanding community annotation of uncollected sources. In: Proceedings of the First SIGMM Workshop on Social Media, pp. 3–10 (2009)

  22. Speriosu, M., Sudan, N., Upadhyay, S., Baldridge, J.: Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the First Workshop on Unsupervised Learning in NLP, pp. 53–63 (2011)

  23. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405 (2011)

  24. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

  25. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł, Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 60006010 (2017)

    Google Scholar 

  26. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. ArXiv Preprint arXiv:1710.10903 (2017)

  27. Wang, L., Niu, J., Yu, S.: Sentidiff: combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Trans. Knowl. Data Eng. 32(10), 20262039 (2019)

    Google Scholar 

  28. Wang, T., Chen, Y., Wang, Y., Wang, B., Wang, G., Li, X., Zheng, H., Zhao, B.Y.: The power of comments: fostering social interactions in microblog networks. Front. Comput. Sci. 10(5), 889907 (2016)

    Article  Google Scholar 

  29. Wei, J., Liao, J., Yang, Z., Wang, S., Zhao, Q.: Bilstm with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing 383, 165173 (2020)

    Article  Google Scholar 

  30. Wu, F., Huang, Y., Song, Y.: Structured microblog sentiment classification via social context regularization. Neurocomputing 175, 599609 (2016)

    Article  Google Scholar 

  31. Yang, J., Zou, X., Zhang, W., Han, H.: Microblog sentiment analysis via embedding social contexts into an attentive lstm. Eng. Appl. Artif. Intell. 97, 104048 (2021)

    Article  Google Scholar 

  32. Ye, X., Dai, H., Dong, L., Wang, X.: Multi-view ensemble learning method for microblog sentiment classification. Expert Syst. Appl. 166, 113987 (2021)

    Article  Google Scholar 

  33. Zhang, Q., Zhang, Z., Yang, M., Zhu, L.: Exploring coevolution of emotional contagion and behavior for microblog sentiment analysis: A deep learning architecture. Complexity 2021, 6630811 (2021)

  34. Zhao, C., Wang, S., Li, D.: Exploiting social and local contexts propagation for inducing Chinese microblog-specific sentiment lexicons. Comput Speech Lang 55, 5781 (2019)

    Article  Google Scholar 

  35. Zhao, K., Zhang, Y., Zhang, Y., Xing, C., Li, C.: Learning from user social relation for document sentiment classification. In: International Conference on Database Systems for Advanced Applications, pp. 86–103. Springer (2019)

  36. Zou, X., Yang, J., Zhang, J.: Microblog sentiment analysis using social and topic context. PLoS One 13(2), e0191163 (2018)

    Article  Google Scholar 

  37. Zou, X., Yang, J., Zhang, W., Han, H.: Collaborative community-specific microblog sentiment analysis via multi-task learning. Expert Syst. Appl. 169, 114322 (2021)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (nos. 61903056 and 61702066), and the Chongqing Research Program of Basic Research and Frontier Technology (nos. cstc2019jcyj-msxmX0681, cstc2019jcyj-msxm1262, and cstc2021jcyj-msxmX0761).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Chenquan Gan & Xiaopeng Cao

  2. School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Qingyi Zhu

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  1. Chenquan Gan
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  2. Xiaopeng Cao
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Correspondence to Chenquan Gan.

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Gan, C., Cao, X. & Zhu, Q. Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks. Multimedia Systems 29, 1161–1172 (2023). https://doi.org/10.1007/s00530-023-01048-3

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