COVID-19 Public Tweets Sentiment Analysis using TF-IDF and Inductive Learning Models
The fact of appearing of the handheld devices offers forthright entree to the internet and social networking sites. Sentiment analysis and opinion mining is the study of sentiments or opinions shared by different users in social networking sites like, Twitter, Facebook, Reddit, Instagram etc., on diverse social phenomena. In this article, sentiment analysis of different tweets on the ongoing epidemic COVID-19, Corona virus disease is performed. COVID-19 is declared as epidemic by the World Health Organization (WHO) in the mid of March 2020. The statistical and machine learning based analyses are implemented on 40,000 tweets, which were collected in two different mutually exclusive time frames. Tweets are collected from Twitter site between 3/07/2020 to 11/07/2020 and 01/08/2020 to 06/08/2020, using Tweepy python library. Various Python based libraries are applied for data acquisition, data pre-processing and data analysis processes. As a data pre-processing phase initially sentences are cleaned. Then by calculating the polarity and subjectivity measures tweets are categorized into three groups (viz., negative, neutral, and positive