This project is part of the Data Science and Visualization Bootcamp at University of California San Diego Extension.
In this project, we (Stephen Hong, Trevor Kleinstuber, Laura Paakh May, and Thompson Tang) used a deep neural network trained on labeled twitter data to analyze the sentiments of tweets, and then tested the neural network to determine what zodiac signs are being talked about the most positively.
- Inferential Statistics
- Machine Learning
- Data Visualization
- Predictive Modeling
- Python
- D3
- Pandas, jupyter
- HTML
- JavaScript
- Tweepy Library
- Vader Library
- Twitter Developer App
- Twitter API connection
A full explanation of our process can be found here. We are working on a consumer-friendly visualization in the form of a website.
- frontend developers
- data exploration/descriptive statistics
- data processing/cleaning
- statistical modeling
- writeup/reporting
- Clone this repo (for help see this tutorial).
- API call script is being kept here.
- Raw Data is being kept here within this repo.
- Data processing/transformation scripts are being kept here
- Cleaned data is kept here.
- We visualized this data on Tableau here and the script for our website (still under development) can be found here.
