https://github.com/vidiptvashist/opinion-mining-and-classification-of-new-national-education-policy-using-twitter-data

Opinion mining, also known as sentiment analysis, is used to ascertain public opinion. It is a technique for natural language processing. Sentiment analysis can be characterised as a technique that utilises Natural Language Processing (NLP) to automate the mining of attitudes, opinions, perspectives, and emotions from text - tweets.
https://github.com/vidiptvashist/opinion-mining-and-classification-of-new-national-education-policy-using-twitter-data

machine-learning nlp sentiment-analysis

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Opinion mining, also known as sentiment analysis, is used to ascertain public opinion. It is a technique for natural language processing. Sentiment analysis can be characterised as a technique that utilises Natural Language Processing (NLP) to automate the mining of attitudes, opinions, perspectives, and emotions from text - tweets.

• Host: GitHub
• URL: https://github.com/vidiptvashist/opinion-mining-and-classification-of-new-national-education-policy-using-twitter-data
• Owner: vidiptvashist
• Created: 2022-07-21T19:18:15.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-21T19:21:04.000Z (over 2 years ago)
• Last Synced: 2024-11-13T23:30:35.873Z (2 months ago)
• Topics: machine-learning, nlp, sentiment-analysis
• Language: Jupyter Notebook
• Homepage: http://www.dspace.dtu.ac.in:8080/jspui/handle/repository/19348
• Size: 16.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Opinion-mining-and-classification-of-New-National-Education-Policy-using-Twitter-data

With the growth of technology in recent years, there has also been a rapid increase in the 

usage of social media sites to exchange information and beliefs. Opinion mining, also known 

as sentiment analysis, is used to ascertain public opinion. It is a technique for natural 

language processing. Sentiment analysis can be characterised as a technique that utilises 

Natural Language Processing (NLP) to automate the mining of attitudes, opinions, 

perspectives, and emotions from text, audio, tweets, and database sources. We gathered data 

from the microblogging website Twitter regarding the New Education Policy (NEP 2020-

2022) in order to have a better understanding of the public mood on a national level.

Convenience of social media especially Twitter is that it empowers the swift collection of 

information about the opinions of the public and individual users on current continuing 

topics. We employed models to classify and assess the emotion elicited by a compilation of 

around 22,000 tweets about the topic of New Education Policy (NEP). We carried out our 

investigation using CountVectorizer and TF-IDF and discovered that CountVectorizer 

outperformed TF-IDF. Additionally, we used Naive Bayes, Decision Trees, Random Forests, 

Logistic Regression, Gradient Boosting, and Support Vector Machines, and discovered that 

Logistic Regression provided the highest assorting accuracy