Sparking Joy with Natural Language Processing
Exploring Natural Language Processing with Marie Kondo and Reddit
April 2019
Check it out on GitHub:
Skills Utilized: Python | Web APIs | NLP | Machine Learning | Binary Classification | Regex | Flask
Does it spark Joy? Try out the web app!
Determine if an excerpt of text "sparks joy" or not!
(According to Logistic Regression, Naïve Bayes, and Voting Classifier classification models.)
Problem Statements:
- Based on text data from posts, can the model correctly classify posts from r/konmari? vs. r/hoarding?
- What words are most important in identifying r/konmari? posts?
- Are there certain posts with which the model has trouble? Why?
- Can the model be generalized outside Reddit?
Executive Summary:
The project primarily utilizes two classification models:- Naïve Bayes (Multinomial)
- Logistic Regression
Additionally, a voting classifier model was run to see if a more conceptually and computationally complex model produced better results.
The NLP models herein have proven successful at accurately predicting whether a post or not a post from the r/konmari subreddit, though some overfitting is present.
Data Gathering:
I pulled Reddit posts using either the Reddit API (via the Python Reddit API Wrapper) or the Pushshift API.The initial dataset included 4,936 reddit posts with approximately 53% being from r/konmari.
Conclusions:
The NLP models that sought to classify r/hoarding and r/konmari, all showed strong results with testing scores (r-squared) of 0.95 or higher.Once other similarly 'tidy' subreddits were introduced, accuracy scores dropped and overfitting increased, as the new posts were much more similar to the positive target than the existing observations of the negative target class.
Stemming and lemmatization was performed and although not dramatically different than the base models, a stemmed corpus might be performed due to a slight reduction in variance.
A further analysis was performed by taking transcripts of the Marie Kondo's Netflix show (scraped from https://www.springfieldspringfield.co.uk) and feeding each episode into an existing base NLP model. Interestingly, each episode of the show had differing 'levels of Konmari-ness' and each were correctly identified as being a part of the 'konmari' class.
Further Improvements:
In order to cut down on the variance, I would like to gather additional data to feed into the model. Additionally, given the interesting results with the Netflix transcripts, I'd like to test the improved model on additional non-reddit data for a more generalized model of 'Konmari-ness'.Get In Touch
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