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Cover image for PapaReddit - scrape, analyze and read Reddit comments
Ilya Nevolin
Ilya Nevolin

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PapaReddit - scrape, analyze and read Reddit comments

What I built

An easy and minimalistic Reddit comments scraper, analyzer and reader that runs on any device in the browser.

Category Submission:

Built for Business

App Link

https://nevolin.be/papareddit/
or
https://papa-reddit-midvl.ondigitalocean.app/

Screenshots

reddit scraper

Description

  1. Open the app URL.
  2. It will automatically load 30 random comments.
  3. Use the input fields to customize the results:
  • Limit: the max number of results you wish to retrieve, this is not guaranteed and limited to 1000 by Reddit's API.
  • Subs: a list of subreddits space separated ("all" is default).
  • Words: a list of words to match in the title or comment's body, these are space separated.

Link to Source Code

https://github.com/healzer/PapaReddit

Permissive License

MIT

Background

This tool was designed to quickly analyze and skim over comments made on Reddit, which can be useful for research, marketing and sales.

As entrepreneurs and developers we often have that craving to find some interesting piece of information, that hopefully leads towards a new product, business, service, feature, improvement, ... Instead of going to Facebook/Instagram/YouTube to satisfy that urge with cheap and useless information, we should focus on being user centered: read and study your target audience, what they are saying, thinking and wanting.

Using Reddit's website directly can also be slow and contains a lot of noise (colors, pictures, ads, etc.). This solution tries to minimize the noise to a minimum.

How I built it

This solution was built using JavaScript/jQuery with HTML/CSS. You can easily extend it with new features (pattern matching, NLP, ML, etc.).

You can also use parts of the code in NodeJS if you wish to centralize the API calls and/or data analysis.

This is pretty much a static website with some JavaScript, it can be hosted on a free Digitalocean App plan.

Additional Resources/Info

You could further extend this with NLP and/or sentiment analysis. Or use the comments to train neural networks for pattern matching, text generation or something crazy.

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