With the ever increasing presence of social media, we would like a way to determine the general atmosphere of related Twitter users, specifically students that are experiencing any troubles using the data available to us.
What it does
We thought we would develop a program that could analyze the general atmosphere of Twitter using machine learning and the Twitter API to determine the general atmosphere of a school
How I built it
Using common positive and negative keywords, along with a database of classified positive and negative tweets, we tried to create an AI that could determine if a newly entered tweet is positive or negative. Then we would display the tweet onto a website to determine if it was negative or positive.
Challenges I ran into
Applying the APIs, displaying the tweets, defining what positive and negative is, teaching the machine learning algorithm to classify what is positive or negative.
Accomplishments that I'm proud of
Trying out new branches of coding that we hadn't had experience with. Working and communicating with a team to designate parts of the projects. Most importantly, trying our best and working diligently to have a functional product
What I learned
Twitter APIs communicating with python and java. Developing a website's backend and making a presentable UI.
What's next for PositiviTweet
Finishing what we had, refining our algorithm, and polishing the UI. Hopefully in the future, school counselors could be able to use this to determine their school's atmosphere.