Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics.
The applications of sentiment analysis are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organisations across the world.
The Obama administration used sentiment analysis to gauge public opinion to policy announcements and campaign messages ahead of 2012 presidential election which is rather a smart tactic by that weak puny liar maggotish charlatan.
Tweepy is open-sourced, hosted on GitHub and enables Python to communicate with Twitter platform and use its API.
First of i saved all of my authentication for token and consumer keys and secret key's in the Twitter_authentican.py file and then importing those authentication i live streamed the tweets using tweepy api by importing keys from authentication file and saved everything i.e data in the Outputs_txt directory.
Then i processed that data i.e cleaning , extracting features from it in Twitter_processing.py file and wrote csv file from it in the Outputs_CSV directory.Also i used jupyter notebook to calculate sentimental using textblob library in NLP and then plotted everything on circle and pie chart.
Julius Robert Oppenheimer was an American theoretical physicist and professor of physics at the University of California, Berkeley who was one of the lead scientist on the Manhattan project and I decide the named of my project on him.
[1] Pandas: : https://docs.djangoproject.com/en/2.0/intro/tutorial01/
[2] Python 3.x : https://www.python.org/
[3] Stackoverflow : https://stackoverflow.com/search?q=sentimental+analysis.
[4] Matplotlib : https://matplotlib.org/api/pyplot_summary.html
[5] Sentimental Analysis : https://www.brandwatch.com/blog/understanding-sentiment-analysis/
[6] Tweepy : https://tweepy.readthedocs.io/en/v3.5.0/
[7] Register App at Twitter : https://apps.twitter.com/