Sentiment Analysis and Emotion Detection of COVID-19 Geo-Tagged Twitter Data

The COVID-19 pandemic has caused significant emotional distress among people worldwide, including anxiety, stress, and depression. This paper presents an analysis of public tweets from various regions of the United States to explore the emotional distress experienced by individuals during the COVID-19 pandemic. By utilizing advanced natural language processing techniques and machine learning algorithms, emotional indicators and anxiety levels were extracted from the COVID-19 Geo-Tagged Tweets. The study revealed significant implications of the pandemic on people's mental health across different regions. Moreover, the findings highlight the association between mental health and the pandemic, suggesting the potential for leveraging this approach to predict mental health outcomes during similar crises.