Sentimental Analysis of COVID-19 Tweets Using Semantic Approach

Sentiment analysis is an approach to determine individual’s emotional state by investigating text from web. Due to COVID-19 the entire worldwide population was driven by fear of erroneous information across the web. This article explores public opinion and examines people's attitudes and emotions using tweets during the pandemic. To analyze the changes in sentiments and emotions, tweets from Indian users in 2020 were collected during April 11-14, June 4-8, and August 20-24. The proposed work compares TextBlob and NRC Lexicon sentiment analysis methods. TextBlob performs better in sentiment classification whereas the NRC Lexicon technique evaluates emotional states in larger depth. The proposed work examines the sentiment variations from three different tweets time slots and compares the results from both experimental methods. The outcomes from both the methods indicate that neutral sentiments are the majority and positive sentiments overshadow negative sentiments