Personality Analysis Using Machine Learning

The goal of this project is to develop a software capable of performing sentiment analysis on bodies of text with a given author. This tool will mimic IBM Watson's Personality Insights Service - a popular online resource and application programming interface (API) for personality classification.

Background
There are many sentiment analysis tools available for public use, however, many have significant drawbacks, are not well documented, or perform classification with relatively poor accuracy. We plan to produce the initial framework needed to create a new sentiment analysis tool that remedies many of these problems. Specifically, we will focus on web mining Twitter tweets, storing tweets in a database with relevant statistical information, and training a machine from the collected data.

Deliverables

 * Twitter mining tool capable of collecting and combining numerous tweets from verified users/authors into text samples
 * Text samples passed through IBM Watson Personality Insights Service
 * Database with large collection of authors, indexed IBM Watson output, and statistics
 * Machine trained on collected data

Machine Learning
Two machine learning (ML) approaches, Gaussian Process (GP) and Convolution Neural Network (CNN), are currently being considered. A comparison will be made to determine the best option (work in progress).