Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-6068-0_31
Reference16 articles.
1. Nayak, J., Mishra, M., Naik, B., Swapnarekha, H., Cengiz, K., Shanmuganathan, V.: An impact study of COVID-19 on six different industries: automobile, energy and power, agriculture, education, travel and tourism and consumer electronics. Expert. Syst. 39(3), e12677 (2022)
2. Priyanka, V.: Twitter sentiment analysis using deep learning models (2021). https://doi.org/10.5281/zenodo.5035477
3. Villavicencio, C., Macrohon, J.J., Inbaraj, X.A., Jeng, J.H., Hsieh, J.G.: Twitter sentiment analysis towards covid-19 vaccines in the Philippines using Naïve Bayes. Information 12(5) (2021)
4. Habib, M.W., Sultani, Z.N.: Twitter sentiment analysis using different machine learning and feature extraction techniques. Al-Nahrain J. Sci. 24(3), 50–54 (2021)
5. Samsir, D., et al.: Naives Bayes algorithm for twitter sentiment analysis. J. Phys. Conf. Ser. 1933(1) (2021)
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