Twitter is a rich source of live information. Is it possible to run sentiment analysis on what the world is thinking as an event unfolds over time? Could we track Twitter data and see if it correlates to news that affects stock market movements? These are some of the questions that we will answer in this podcast episode. 

There are 6 steps for mining Twitter data for sentiment analysis of events that we will cover:

1) Get Twitter API Credentials
2) Setup API Credentials in Python
3) Get Tweet Data via Streaming API using Tweepy
4) Use out-of-the-box sentiment analysis libraries to get sentiment information
5) Plot sentiment information to see trends for events
6) Set this up on AWS or Google Cloud Platform

This episode covers information about saving the tweets in a database, and using them to plot sentiment information.

Corresponding Blog Post With Code: https://towardsdatascience.com/mining-live-twitter-data-for-sentiment-analysis-of-events-d69aa2d136a1?source=friends_link&sk=e06ae49f4ce6fb52157ea0eaee72f4c4
Tweepy: https://github.com/tweepy/tweepy
TextBlob: https://textblob.readthedocs.io/en/dev/
Vader Sentiment: https://github.com/cjhutto/vaderSentiment
Set up AWS instance: https://aws.amazon.com/ec2/getting-started/
Set up GCP instance: https://cloud.google.com/compute/docs/quickstart-linux

My Twitter Profile: https://twitter.com/sanket107
Thanks for listening!