Reddit Sentiment Analysis

Enter a subreddit and perform sentiment analysis. Returns the percentage of positive, negative, and neutral headlines in a subreddit. Also displays the five most positive and the five most negative subreddits. See the Github Repo here. See the Colab Notebook here.  

 

I made this project over the course of a weekend for a Hackathon in 2020. It was one of my first larger computer science projects. I learned a lot from this project. It was my first time creating a website with backend, working with a large website’s API and data, applying ML models on data from the wild, and deploying a website on Heroku (unfortunately the Heroku website is no longer available because Heroku stopped offering free dynos).

How does this work?

A natural language processing model is used to determine the sentiment of a subreddit from the top 50 newest titles. The user is able to type a subreddit, which will then be ran through the machine learning model. The model classifies titles as positive, negative, or neutral and returns the percentage breakdown of a subreddit, along with the top five most positive and top five most negative posts.

Methodology for Nerds

Our first step was to explore the Reddit API and to examine our data. We then had to figure out the best machine learning model for the task. We ended up using VADER, a pretrained model from NLTK that specializes in identifying the sentiment of social media text. We tested this model on several subreddits and explored the results, before coding this into a python script, and then into a website. To see more about the model building process, check out the interactive Colab notebook.

Future Steps

Using this same user interface, we could test out different models that may bring us more informative analyzes of subreddits. For example, we could train a model that could detect fake news or hate speech and give scores for subreddits. Additionally, we could change our methodology in deploying our model on subreddits. Instead of looking at titles, it may be more informational to take into account comments or even upvotes.