Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification

• Published in: SN computer science 2021, Vol.2 (3), p.227-227, Article 227
• Main Authors: Ramya, B. N., Shetty, Shyleshwari M., Amaresh, A. M., Rakshitha, R.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Coronaviruses; COVID-19; Data mining; Data Science and Communication; Data Structures and Information Theory; Information Systems and Communication Service; Machine learning; Original Research; Pattern Recognition and Graphics; Scientific visualization; Sentiment analysis; Social networks; Software Engineering/Programming and Operating Systems; Stratification; Viral diseases; Vision
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-021-00625-5

In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naïve Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets.