LeBron James: An NLP Exploration

Overview

reddit.com/r/nba is the subreddit dedicated to the National Basketball Association (NBA). With over 1,420,000 subscribers at the time of writing - its membership dwarfs all other major American sports leagues' subreddits in size and even the 1,217,000 subscriber-strong r/soccer. Subreddit submissions cover everything from potential recruits playing in the NCAA and for international teams, game threads for all NBA games played, breaking NBA news, speculative questions by redditors, light-hearted jokes, and everything in between.

Unsurprisingly, one of the most well-known players currently playing in the NBA, LeBron James, is the direct or an indirect focus of many forum submissions. This project is geared toward finding more insights about what redditors are talking about when they talk about LeBron.

Natural Language Processing (NLP) techniques are applied to 45,000+ reddit comments from r/nba. The comments are the top 10 reddit posts for any submission with "LeBron" or a derivative of his name in the title. The intent was for these comments to be a proxy for the general sentiment implied by the greater r/nba community, due to the nature of reddit's up and down vote system.

Blog post on the topic can be found here.

Project Design

Upon obtaining the reddit comments, extensive NLP preprocessing techniques such as setting to lowercase, punctuation removal, tokenization, reducing to English words only removing stop words, and stemming. The words are then CountVectorized to move into a term-frequency matrix, and from there various models are applied to achieve exploration goals.

The goal of this project is to perform NLP techniques on the reddit comments to glean interesting insights. Specifically:

• Topic Modeling - derive topics from the comments
• Sentiment Analysis - sense the polarity of the comments
• Time Series - overlay time series with the initial two goals to see any changes over time and how they map to real-life events

Tools

• Python:
  • Data cleaning: Pandas, Joblib
  • Data analysis: Numpy, Pandas, Scikit-learn, Jupyter
  • NLP tools: NLTK, Scikit-learn
  • Presentation: Matplotlib, Seaborn
  • API: Reddit API, PSAW for Pushshift, PRAW
• Google Slides

Data

Reddit has an API I intended to use to gather all my comments. PRAW is a popular wrapper that makes accessing the API simple. Shortly after using PRAW, I learned that reddit only allows the most recent 1000 posts from a subreddit to be obtained. As I hoped to reach back to 2008 and potentially 10,000s or 100,000s of posts, this would not do.

I identified two possible solutions. One was using Google's BigQuery to obtain the data I wanted. There is a maintainer who collects and stores all reddit submissions and comments in a SQL database available there.

I chose my second option, which was using Pushshift.io. Pushshift is a service that catalogs more reddit info than the reddit API uses. Combining a Python Library for interacting with Pushshift called PSAW, I was able to download the all the comments I wanted from 2011-2018. Unsure why I could not go back to 2008, but the 45,000 comments I collected was enough data to work with.

Results

After numerous iterations, applying a Latent Dirichlict Allocation (LDA) algorithm with 16 topics resulted in the most interpretable topic creations. While these were far from perfect - about half of the groups I could not pull meaning from whatsoever - I did identify some clear groups such as "best player ever", "free throws", "greatest of all time comparison / michael jordan / kobe bryant", and "best player in the league".

Additionally, I then performed a KMeans Clustering unsupervised algorithm to the document-topic matrix output by LDA. Based on inertia inflection, the best # of clusters was 17 - quite similar to the number of LDA topics. Upon checking the documents in these clusters, some of the clusters directly matched LDA topics. Notably, the largest cluster was full of jokes/puns/quips.

Finally, I performed a sentiment polarity analysis on the dataset using the NLTK Vader implementation. It showed remarkable consistency across time of the comments not tending to carry much positive nor negative feelings. However, in June 2012 there was a noticeable uptick in positive feelings; this also is the time when LeBron James won his first NBA championship.

What I would do differently

I would change my dataset constraints. The first change would be to extract all highly up voted comments from r/nba that contained LeBron or a variation of his name. Secondly, I would require all comments selected have at least 25 or so words. I believe this would filter out many of the jokes/puns/quips/one-liners that occupied much of my dataset and for my use case essentially amounted to unwanted noise.

With this new data, I would want to improve upon the topic modeling. By either improving LDA results or finding better topics from NMF or other algorithms my comments would be better assigned to like categories.

Also, I would like to spend more time mapping real-life events to a surge in comments within specific categories. This would be an interesting experiment to see how well the NLP techniques capture the real world events being discussed in r/nba.

Finally, think more about a use for a well functioning NLP pipeline with this data. Nothing came to mind during this project, except I was exploring a topic I like very much.

Appendix

Repository Organization

Name	Description
cleaning_preprocessing.ipynb	Inputs the raw data/raw/ten_comments_raw.csv csv file. Performs cleaning and NLP preprocessing stesp to prepare the data for NLP modeling.
nlp_processes.ipynb	Inputs a cleaned data table. Performs NLP analysis after preprocessing such as LDA topic creation and KMeans Clustering.
plot_creation_notebook.ipynb	Creates all plots used in presentation and blog post.
reddit_api.ipynb	Script to gather reddit comments using Pushshift.io and Reddit API and save them to a csv file as the "raw" data for this project.
data/	Directory contains raw and cleaned data.
legacy_ideas	Code that was used in early analysis to had down a few different paths, but ultimately not implemented in final models or plots. This is messy and unfinished.
pickled_models/	This folder contains best version of all classifying models created (SK-Learn) pickled in .joblib format.
plots/	This folder contains saved plot images created in plot_creation_notebook.ipynb. These images are mostly used in the presentation and blog post.
reports/	Contains original proposal, summary of project, and presenation slides for the project.
utils.py	Contains some larger functions used in the notebooks.

Dataset Features

comment_text	created_time	reddit_score	comment_author	parent_submission_text	parent_author
string	datetime	int	string	float	float