RateMyProfessor is subject to response biases - students normally comment about their professors when they fail a class or love the professor. By summarizing professor's ratings, we can reduce the amount of outliers in the rating of a professor in order to give students a more accurate depiction of that professor.
What it does
This application allows the user to select a professor and generates a summary of that professor, based on that professor's RateMyProfessor profile.
How I built it
The user selects an option from the drop-down menu in the website we built, using React. A python script then scrapes the web to aggregate reviews on RateMyProfessor, which are then passed to other methods which use NLP to tokenize and create a summary from the reviews.
Challenges I ran into
Accomplishments that I'm proud of
Learning new technologies - we implemented a lot of software (a user front end, a python web-scraping script, and NLP sentiment analysis) in order to create a cool project.
What I learned
See above - a myriad of new technologies.
What's next for Summarize-My-Professor
Working on getting user input to be passed successfully from the frontend to the python script.