Hugging Face's logo

Datasets:
emilpartow
/
reddit_finance_posts_apple-tesla-microsoft

Tasks:
Feature Extraction
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
Tags:
finance
reddit
social-media
sentiment-analysis
apple
tesla
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
1
reddit_finance_posts_apple-tesla-microsoft / README.md
emilpartow's picture
emilpartow
Update README.md
c2a33b9 verified
preview code
|
raw
history blame contribute delete
3.17 kB
metadata
license: cc-by-4.0
task_categories:
  - feature-extraction
language:
  - en
tags:
  - finance
  - reddit
  - social-media
  - sentiment-analysis
  - apple
  - tesla
  - microsoft
pretty_name: Reddit Finance Posts Sample Dataset (Apple/Tesla/Microsoft)
size_categories:
  - 10K<n<100K

Reddit Finance Posts Dataset (Apple, Tesla, Microsoft)

This dataset contains 12046 Reddit Posts collected from 20 finance-related subreddits via the Reddit API using the keywords Apple, Tesla, and Microsoft. The included subreddits are:

stocks, wallstreetbets, investing, StockMarket, options, RobinHood,
pennystocks, SecurityAnalysis, personalfinance, Dividends, CryptoCurrency,
CryptoMarkets, ETFs, FinancialIndependence, ValueInvesting, quant,
algotrading, forex, economy, Superstonk, spacs, financialplanning

About This Dataset

This is a small-scale, beginner-friendly dataset intended mainly for testing, prototyping, and educational purposes.
It serves as a minimal, ready-to-use sample for anyone who wants to experiment with Reddit finance discussions.

Easy to Extend:
The provided data collection pipeline (see emilpartow/financial-sentiment-pipeline) makes it simple to expand the dataset with more comments, different time frames, additional companies, or other subreddits as needed.

If you need a larger or customized dataset, you can easily adapt and rerun the pipeline to collect more data for your own use case.

Dataset Overview

Each row in the dataset contains metadata and content for a Reddit comment or post, with the following columns:

  • id: Reddit post ID
  • title: Title of the post
  • text: Body text of the post (if available)
  • created_utc: Timestamp in UTC
  • created_datetime: Datetime (ISO format)
  • author: Reddit username
  • score: Reddit score (upvotes minus downvotes)
  • num_comments: Number of comments (for posts)
  • upvote_ratio: Upvote ratio (for posts)
  • flair: Post flair (if available)
  • permalink: Direct Reddit link to the comment or post
  • url: URL (if different from permalink)
  • subreddit: Name of the subreddit
  • company: The matched company keyword (Apple, Tesla, or Microsoft)

Potential Use Cases

  • Sentiment analysis
  • Named Entity Recognition (NER)
  • Text classification
  • Experiments with Reddit and finance text data

Data Collection

All posts were collected using PRAW (Python Reddit API Wrapper).
Full code and data processing pipeline can be found here:
➡️ emilpartow/financial-sentiment-pipeline

Notes

  • No manual or verbal content review was performed: The dataset consists of comments as collected via the Reddit API. There may be offensive, biased, or otherwise inappropriate content present.
  • Please respect Reddit’s API Terms of Use when working with this dataset.

License

CC BY 4.0

Questions or feedback?
Feel free to open an issue or discussion in the linked GitHub repository!