Analyzing the depression and suicidal tendencies of people affected by COVID-19’s lockdown using sentiment analysis on social networking websites

Author:

Sharma Sparsh1,Sharma Surbhi2

Affiliation:

1. Department of Computer Science & Engineering, Baba Ghulam Shah Badshah University, Rajouri 185234, Jammu and Kashmir, India

2. Department of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu & Kashmir, India

Publisher

Informa UK Limited

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Opinion mining and machine learning analysis : What emotions twitter data tell us about telemedicine?;Journal of Statistics and Management Systems;2024

2. SoundMind: A Machine Learning and Web-Based Application for Depression Detection and Cure;Intelligent Systems;2023-10-06

3. A Multilevel Depression Detection from Twitter using Fine-Tuned RoBERTa;2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD);2023-09-21

4. Enumerating happiness index during COVID-19 lockdowns using artificial intelligence techniques;International Journal of Technology Management & Sustainable Development;2023-05-01

5. Epicosm—a framework for linking online social media in epidemiological cohorts;International Journal of Epidemiology;2023-02-27

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