The Power of Elon Mask -- an ECE 143 Project
Elon-Power:
├───archive:
├───sentiment code: contains all of the .py files related to sentiment analysis
├───stocks: contains all of the numerical data on stocks we used
├───stocks code: contains all of the .py files related to stocks analysis
├───trends code: contains all of the .py files related to google trends analysis
├───tweets: contains all of the tweet data we used
└───tweets code: contains all of the .py files related to basic tweet gathering, filtering, and sorting
Elon Musk Tweets (2010 - 2022) | Kaggle
For additional data, the following link has details on using Twitter api for information:
https://blog.quantinsti.com/python-twitter-api/
We plan to apply sentiment analysis by using NLP on twitter texts to measure their sentiment and how that translates to a real world impact factoring in tweet engagement. The real world applications of this project are that it can be used to demonstrate the effects of highly-known public figures’ social media on stocks, sentiments, etc. Companies and agents can use this for marketing purposes. Public figures can use this to view the tangible effects their posts have and determine whether or not they should post about things.
Data Gathering: We filter through Elon Musk’s tweets on a sundry of aspects, such as stocks, policies making, and people, as well as a few other sources as necessary to get the effects of those tweets. We estimate this will take 1.5 weeks.
Sentiment Analysis: Apply NLP on texts of tweets to measure sentiment (1) positive (0) neutral (-1) negative. We estimate this will take 1.5 weeks.
Data Visualization: We plot the graphs of the stock prices, follower counts, keyword searches, etc. before and after Musk’s tweets.