Sentiment Analysis of Lockdown in India During COVID-19 A Case Study on Twitter

Project Code :TCMAPY459

Objective

The main objective of this project is to use RNN or LSTM for sentiment analysis of twitter data during covid-19.

Abstract

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP). Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions. People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst. Mining such content to evaluate people's sentiments can play a critical role in making decisions to keep the situation under control.

In this work, the sentiment analysis of tweets has been performed using NLP and machine learning classifiers. Data have been extracted from Twitter, annotated using Text-Blob and preprocessed using the natural language tool kit provided by the Python. RNN is used for sentiment classification in this project. This study concludes that the majority of Indian citizens are supporting the decision of the lockdown implemented by the Indian government during corona outburst.

Keywords: Supervised Learning, Sentiment Analysis, Natural Language Processing (NLP), Neural Networks, RNN, COVID-19.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:
  • Processor: I3/Intel
  • Processor RAM: 4GB (min)
  • Hard Disk: 128 GB
  • Key Board: Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: Any
SOFTWARE SPECIFICATIONS:
  • Operating System: Windows 7+
  • Server-side Script: Python 3.6+
  • IDE: Jupyter Notebook
  • Libraries Used: Seaborn, Pandas etc.,

Learning Outcomes

  • About Python.
  • About Jupyter Notebook.
  • About Pandas.
  • About Seaborn.
  • About HTML.
  • About CSS.
  • About JavaScript.
  • About Database.
  • About Deep Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Project Development Skills:
  • Problem analyzing skills.
  • Problem solving skills.
  • Creativity and imaginary skills.
  • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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