Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling

Abstract

Social media has become a powerful platform for individuals to express their thoughts, experiences, and emotions. With the advent of the COVID-19 pandemic, social media has witnessed a surge in posts related to the virus and its aftermath. In India, a country severely hit by the pandemic, there is a prevalence of post-COVID patients sharing their experiences on social media. These posts provide valuable insights into the physical and emotional challenges faced by individuals after recovering from COVID-19. This paper aims to explore the prevalence of post-COVID patients on social media in India, the signs and symptoms experienced by these individuals, the correlations between post-COVID sentiment and patient characteristics, and the development of a predictive model for sentiment analysis. The prevalence of post-COVID patients on social media in India is evident through the increased number of posts related to COVID-19 and its aftermath. The surge in COVID-19 tweets was followed by an increased number of posts expressing personal experiences with the virus, indicating a significant presence of post-COVID patients on social media. These posts provide a valuable resource for understanding the challenges faced by individuals after recovering from COVID-19. Post-COVID patients often experience a range of signs and symptoms that can have a significant impact on their physical and mental well-being. Fatigue, shortness of breath, brain fog, chest pain, headache, and other symptoms have been reported as potential long-term effects of COVID-19. Additionally, patients with post-COVID-19 depressive symptoms share psychopathological symptoms, indicating the presence of mental health challenges. These signs and symptoms highlight the need for a comprehensive understanding of the post-COVID experience and the emotional toll it can take on individuals. Predictive modeling plays a crucial role in analyzing sentiment expressed by post-COVID patients on social media. Predictive modeling is a mathematical process that aims to predict future events or outcomes by analyzing relevant historical data. By developing a predictive model for sentiment analysis, researchers can gain valuable insights into the emotional experiences of post-COVID patients and potentially identify patterns or trends. This modeling approach allows for a systematic analysis of the vast amount of data available on social media platforms.