Reddit Stock Sentiment Analysis
Turning retail chatter into ticker-level signal.
Source
Reddit / PRAW
Scorer
VADER
Output
Ticker time series
Metric profile
01 / Signal
Cashtag+
Ticker disambiguation
collision filtering
02 / Signal
VADER
Scoring coverage
tuned for finance slang
03 / Signal
Daily series
Output granularity
per-ticker time series
Overview
An end-to-end NLP pipeline that scrapes finance subreddits, resolves ticker mentions, and scores sentiment per symbol using VADER's rule-based lexicon.
Output is aggregated into time-series sentiment scores suitable for exploratory analysis against price movement.
What makes it work
Ticker resolution
Cashtags and bare symbols are extracted and disambiguated from common-word collisions before scoring.
Lexicon sentiment
VADER handles the informal, emoji-heavy register of retail investing forums far better than generic sentiment models.
Aggregation
Pandas rolls per-comment scores into per-ticker, per-interval sentiment series ready for charting or correlation work.
Architecture
- PRAW scraper across finance subreddits
- Ticker extraction + normalisation layer
- VADER compound scoring per comment
- Pandas aggregation into sentiment time series
Stack
- VADER
- PRAW
- Pandas
NLP sentiment pipeline scraping Reddit to compute ticker sentiment scores using VADER.
Interested?