Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID

Author:

Vidushi ,Jain Anshika,Shrivastava Ajay Kumar,Bhushan Bharat,Hameed Alaa Ali,Jamil Akhtar

Publisher

Springer Nature Switzerland

Reference21 articles.

1. Karami, A., et al.: Twitter and research: a systematic literature review through text mining. IEEE Access 8, 67698–67717 (2020)

2. Aladwani, A.M.: Facilitators, characteristics, and impacts of Twitter use: theoretical analysis and empirical illustration. Int. J. Inf. Manage. 35(1), 15–25 (2015)

3. Li, H., Dombrowski, L., Brady, E.: Working toward empowering a community: how immigrant-focused nonprofit organizations use twitter during political conflicts. In: Proceedings of the 2018 ACM Conference on Supporting Groupwork (ACM 2018), pp. 335–346 (2018)

4. Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 US presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations. Association for Computational Linguistics, pp. 115–120 (2012)

5. Bravo-Marquez, F., Frank, E., Pfahringer, B.: Annotate-sample-average (ASA): a new distant supervision approach for Twitter sentiment analysis. In: 22nd European Conference on Artificial Intelligence (ECAI), vol. 285, pp. 498–506. IOS Press (2016)

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