Faculty Reviews Analytical System

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Inspiration

At the start of every new semester, there are many students who are taking classes under different professors and hence need to know the reviews so that they can choose wisely. Due to a lack of time and knowledge, it is difficult for them to read and analyze the comments or reviews. So we thought of building a system that analyses the review comments from websites such as Rate My Professor and others.

What it does

The Faculty Review Analytical System analyses the review comments and gives us feedback with the polarity in the form of positive, neutral, or negative. Additionally, it also provides the subjectivity of the review comments whose value lies between -1 to 1, where -1 is extremely negative, 0 value is neutral, and 1 is extremely positive.

How we built it

We used HTML, CSS, and Javascript for the front-end development and Flask and Flask Bootstrap for back-end development. Also, for natural language processing functionalities we use the Textblob library and the entire development environment is in Python language.

Accomplishments that we're proud of

We are able to use a system in which anyone can use this Faculty review system and which is beneficial for the student community.

What we learned

We learned Python-based frameworks and got a practical-based approach for the concepts of Natural Language Processing.

What's next for Faculty Reviews Analytical System

  1. At present, the user has to copy or type the comment into the form, but in the future, we will try to analyze the entire data set consisting of faculty review comments.

  2. Additionally, we will try to deploy on cloud services and enhance the UI experience as a fully designed website.

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