Twitter Sentiment Analysis on Coronavirus using Textblob

EasyChair Preprint 2974

Abstract

Social networks are the main resources to gather information about people’s opinions and sentiments towards different topics and issues. People spend hours daily on social media to share their ideas, opinions, and reactions with others, so in this paper, we analyze the sentiments regarding coronavirus disease(COVID-19) because many peoples of different countries are affected by coronavirus that is very critical issue in these days, so analyze the sentiments of different people’s opinion for this disease, we are fetching the twitter streaming tweets related to coronavirus using twitter API and analyze these tweets using machine learning techniques and tools as positive, negative and neutral.  In this paper, we run experiments through Python programming on different tweets using twitter API and NLTK library is used for pre-processing of tweets and then analyze the tweets dataset by using Textblob and after that show the interesting results in positive, negative, neutral sentiments through different visualizations.

Keyphrases: TextBlob, Twitter API, Twitter Sentiment Analysis

@booklet{EasyChair:2974,
  author    = {Chhinder Kaur and Anand Sharma},
  title     = {Twitter Sentiment Analysis on Coronavirus using Textblob},
  howpublished = {EasyChair Preprint 2974},
  year      = {EasyChair, 2020}}