VIT University
Sentiment analysis of covid-19 tweets
SENTIMENT ANALYSIS
In the wake of the COVID-19 pandemic, the need for real-time insights into public sentiment and opinions has become more crucial than ever. Understanding how people are responding to the situation, especially on platforms like Twitter, can provide valuable information for policymakers, healthcare professionals, and the general public.
To address this, our project, “Sentiment Analysis of COVID-19 Tweets in India,” was conceived. By harnessing the power of Natural Language Processing (NLP) and sentiment analysis, we aim to collect and analyze tweets containing relevant hashtags, enabling us to gauge the sentiment and emotional trends surrounding the pandemic in India.
ABOUT
The “Sentiment Analysis of COVID-19 Tweets in India” project is a data-driven initiative designed to provide valuable insights into the prevailing public sentiment in India during the ongoing pandemic. Leveraging cutting-edge NLP techniques, our system scours Twitter for tweets containing relevant COVID-19-related hashtags.
These tweets are then meticulously cleaned and processed to categorize tweets into positive, negative, or neutral sentiments. By making this sentiment analysis data readily available, our project aims to empower decision-makers, and the public with real-time insights into the evolving COVID-19 sentiment landscape in India.
Year
2023
Affiliation
VIT University
Services
NLP, ML, Data Visualization
Project
Data-Science
Description
The “Sentiment Analysis of COVID-19 Tweets in India” project is a crucial tool for monitoring public sentiment during the pandemic. It scrapes Twitter for COVID-19-related tweets, processes them using NLP, and employs a sentiment analyzer library to classify them as positive, negative, or neutral. This project provides valuable insights into how people in India are responding to the ongoing pandemic, aiding decision-makers and researchers in real-time analysis.
In this project, I led a groundbreaking NLP initiative, architecting a Python web crawler to collect and analyze 179,108 COVID-19-related tweets from India. Using Python, Scrapy, NLTK, and Matplotlib, I achieved an impressive 97.14% accuracy in sentiment classification. This highlights my commitment to data-driven insights and technology-driven solutions during critical times.