Reddit Sentiment Analyzer

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  Image of Sentiment analysis of the r/learnprogramming subreddit.

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  Image of where you enter the subreddit.

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  Image of the python script implementation for r/cats

Inspiration

Reddit and social media. We found the current events and the large amounts of fake news going on quite disturbing. We tried to make a software that lets you know how trustworthy a subreddit on reddit is based on the information being posted there.

What it does

Receives a user inputted subreddit and returns percentages of positive, negative, and neutral headlines. Also shows the five most positive and five most negative headlines.

How we built it

We used python's natural language toolkit for the pretrained VADER machine learning model. We explored the machine learning model and our methodology in a Colab notebook. We used html and css for the frontend, and Flask for the backend. We used Heroku to host our website.

Challenges we ran into

We had trouble connecting the front end and back end of our website.

Accomplishments that we're proud of

We made a decently accurate sentiment analysis model and applied it to subreddits. We created a user interface to run the model through a website. We got our website running and we made something cool out of it.

What we learned

We learned flask, reddit api, nltk. Now we can turn any of our Python scripts into fully functional websites!

What's next for Reddit Sentiment Analyzer

We want to expand our machine learning models to be more informative. Instead of looking for positive, neutral, and negative sentiments, a different model may be used to detect fake news or hate speech. We can also expand our model to detecting sentiments in other social media such as Facebook and Twitter.

Built With

• css
• flask
• heroku
• html
• nltk
• python
• redditapi