akshi8/classifying_reddit_posts

Leveraging NLP and supervised learning methods to classify posts scraped via Reddit's API

Problem Statement

Develop a classification model that can distinguish which of two subreddits, (r/menstruation or r/BabyBumps), a particular post belongs to.

Executive Summary

We cleaned and analyzed 2 features (post text and post title) which we engineered by way of Count Vectorizing. We removed English stop words, stripped ASCII accents for both features. This resulted in a 393 features which we fed into 2 variations of Logistic Regression, Decision Tree, Random Forest and Multinomial NB models each.

My second Multinomial Naive Bayes model performed the best. With the best parameters being?-?alpha: 0 and fit_prior: False. The accuracy score on the training data was 92.4% and on unseen data it came out to be 92.2%. This means our model is slightly and probably inconsequentially overfit. This also means that 92.2% of our posts will be accurately classified by our model.

Recommendations

• Go with Multinomial Naive Bayes Model

  • Version 2 handled bias/variance the best

• Spend more time with current features

  • e.g. Engineer a character or word length feature

• Explore new features

  • Upvotes
  • Post comments

Our presentation can be found here. I also have an article featured on Towards Data Science that walks you through my data science process.