Sentiment Analysis of Bangladesh-specific COVID-19 Tweets using Deep Neural Network

Nowadays, social media became a tracker of the COVID-19 disease which reflects the status of the COVID-19 outbreak in the world. Although it is important to know the impact of COVID- 19 on the sentiment of mass people for the government and the policymakers in order to address peoples’ needs and take emergent decisions during such crisis time, not many studies have been conducted regarding this issue. Moreover, very few studies were conducted on sentiment analysis during the COVID-19 pandemic in the context of Bangladesh. The purpose of this study is to estimate the impact of the COVID-19 outbreak on the sentiment of the Bangladeshi people through a machine learning approach. To achieve this goal, COVID-19 tweets were collected over a specific period and then build a deep learning classifier, having an average area under the curve (AUC) of 0.76. The study analyzes the spread and estimates various public emotions during the outbreak. And reveals that a significant number (55%) of people had negative sentiment regarding COVID-19, whereas, 38% and 7% of people had positive and neutral sentiment respectively. This study also found that people’s involvement with social media increases as the number of active COVID-19 cases increases. Moreover, this study identified people’s sentiment towards some important concerns regarding the COVID-19 pandemic.