Machine Learning Technique with Spider Monkey Optimization for COVID-19 Sentiment Analysis

The coronavirus (COVID-19) has unquestionably posed one of the most difficult problems in human history. As of May 5, 2021, the global death toll from the on going COVID-19 epidemic was at 150 million. Monitoring, managing, and finally eradicating the disease can be made easier by understanding the mood represented in people’s social media remarks. It is the goal of this research to educate social media policy, such as how much reasonableness is required to curb misrepresentation on social media. This research also examines people’s opinions on COVID-19 over social media sharing and interaction on Twitter. For our studies, we’ve generated a new large-scale sentiment data collection called COVIDSENTI that includes 90,000 tweets about the COVID-19 epidemic from February to March of the following year. We classified the tweets based on their emotional tone: good, negative, or neutral. In this work, ensemble machine learning classifier and uses the Spider Monkey optimization (SMO) for selecting the optimal features. The raw datasets are cleaned by using pre-processing techniques and important features are extracted by using feature extraction practises. We used multiple parameter measurements to assess the gathered tweets for sentiment categorization.