Abstract
Micro-blog is an important medium of emergency communication. The topic and emotion analysis of micro-blog is of great significance in identifying and predicting potential problems and risks. In this paper, a collaborative analysis model of emotion and topic mining is constructed to analyze the users’ sentiment and the topics they care about, Firstly, we use SO-PMI to construct domain sentiment lexicon and extract topics with LDA. Then we use the collaborative model to analyze sentiment and topic. The results showed that the model we proposed can present the features of sentiment and topic of user concerns. And through text clustering and sentiment analysis, it is found that the attitude of users towards the COVID-19 has gone through three stages, namely, a period of fluctuating tension and anxiety, a period of slowly rising solidarity and a period of stable self-confidence with little fluctuation, on the whole, positive is greater than negative, positive than negative state.
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References
Van, L.T., Nguyen, Q.H., Dao, T.: Emotion recognition with capsule neural network. Comput. Syst. Sci. Eng. 41(3), 1083–1098 (2021)
Oglah, M., Asghar, S.: Sentiment analytics: extraction of challenging influencing factors from covid-19 pandemics. Intell. Autom. Soft Comput. 30(3), 821–836 (2021)
Albahli, A.S., et al.: COVID-19 public sentiment insights: a text mining approach to the Gulf countries. Comput. Mater. Continua 67(2), 1613–1627 (2021)
Gu, Z., et al.: Epidemic risk assessment by a novel communication station based method. IEEE Trans. Netw. Sci. Eng. 9(1), 332–344 (2021)
Tian, Z., Luo, C., Lu, H., Su, S., Sun, Y., Zhang, M.: User and entity behavior analysis under urban big data. ACM/IMS Trans. Data Sci. 3(1), 1–19 (2020)
Qiu, J., Chai, Y., Tian, Z., Du, X., Guizani, M.: Automatic concept extraction based on semantic graphs from big data in smart city. IEEE Trans. Comput. Soc. Syst. 7(1), 225–233 (2020)
Xiong, X., et al.: An emotional contagion model for heterogeneous social media with multiple behaviors. Physica A Stat. Mech. Appl. 490, 185–202 (2018)
Xu, D., Tian, Z., Lai, R., Kong, X., Tan, Z., Shi, W.: Deep learning based emotional analysis of microblog texts. Inf. Fusion 64, 1–11 (2020)
Tang, Y., Liu, W., He, Y., Zhang, Y., Zhang, F.: The development of generalized public bicycles in China and its role in the urban transportation system. J. Internet Things 2(3), 101–107 (2020)
Xu, L., Lin, H., Pan, Y.: Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 27(2), 180–185 (2008)
Luo, C., Tan, Z., Min, G., Gan, J., Shi, W., Tian, Z.: A novel web attack detection system for internet of things via ensemble classification. IEEE Trans. Ind. Inform. 17(8), 5810–5818 (2021)
Roy, K.C., Ahmed, M.A., Hasan, S., Sadri, A.M.: Dynamics of crisis communications in social media: spatio-temporal and text-based comparative analyses of twitter data from hurricanes Irma and Michael. In: 17th International Conference on Information Systems for Crisis Response and Management, pp. 812–824. IEEE, Virginia, USA (2020)
Sreenivasulu, M., Sridevi, M.: Comparative study of statistical features to detect the target event during disaster. Big Data Mining Anal. 3(2), 121–130 (2020)
Wang, X.F., Sheng, S., Lu, Y.: Analyzing public opinion from microblog with topic clustering and sentiment intensity. Data Anal. Knowl. Discov. 2(6), 37–47 (2018)
Zhao, C.Y., Wu, Y.P., Wang, J.M.: Twitter text topic mining and sentiment analysis under the belt and road initiative. Libr. Inf. Serv. 19, 119–127 (2019)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of sematic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003)
Blei, D.M., Andrew, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(1), 993–1022 (2003)
Zareie, A., Sheikhahmadi, A., Jalili, M.: Identification of influential users in social networks based on users’ interest. Inf. Sci. 493, 217–231 (2019)
Kim, Y.: Convolutional neural networks for sentence classification. In: 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751. ACL, Doha, Qatar (2014)
Devlin, J., Chang, W.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 4171–4186. ACL, Minneapolis, Minnesota (2019)
Zhang, Z.Y., Han, H., Liu, Z.Y., Jiang, X., Sun, M.S., Liu, Q.: ERNIE: enhanced language representation with informative entities. In: 57th Annual Meeting of the Association for Computational Linguistics, pp. 1441–1451. ACL, Florence, Italy (2019)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (No. 62002077), in part by Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515110385) and Guangzhou Science and Technology Plan Project (202102010440).
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Luo, T., Li, R., Sun, Z., Tao, F., Kumar, M., Li, C. (2022). Let the Big Data Speak: Collaborative Model of Topic Extract and Sentiment Analysis COVID-19 Based on Weibo Data. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_22
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