Designing an LSTM and Genetic Algorithm-based Sentiment Analysis Model for COVID-19

The unleashing of Coronavirus on human lives has drastically changed a lot of things. The pandemic has been a difficult time for everybody as people lost their jobs and businesses, the economy dwindled, health issues due to the virus, be it physical or mental, were prevalent. Loneliness, depression, and anxiety caused by lockdown and work from home became the new normal. It, therefore, becomes imperative to study the large amount of social media data using computational methods and gauge the sentiment of people on various policies and strategies undertaken to fight the pandemic and take decisions accordingly. We introduce a Social Media Pandemic Sentiment Model on COVID-19 Twitter dataset to study the sentimental variation in people throughout the duration of pandemic and derive useful results out of it. We also provide an extensive comparative analysis of this model with other conventional states of the art models to display the competence of our model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.