WallStreetBets Sentiment Analysis — Reddit API

Scrapes r/wallstreetbets posts via the Reddit API, applies NLP sentiment scoring to derive Buy/Hold/Sell signals, and produces 6 visualisations showing how retail investor sentiment evolves over time and correlates with trading recommendations.

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Table of Contents

• Project Overview
• Data Collection
• Sentiment Analysis Pipeline
• Results & Visualisations
• Trading Signal Logic
• How to Run
• Files

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Project Overview

r/wallstreetbets (12M+ members) is one of the most influential retail investor communities. Post sentiment on WSB has demonstrably moved stock prices (GameStop, AMC, etc.). This project quantifies that sentiment at scale:

1. Scrape post titles, content, upvotes, and downvotes via Reddit API
2. Score each post with NLP sentiment analysis
3. Classify into Buy / Hold / Sell signals based on score thresholds
4. Visualise temporal patterns, score distributions, and signal-sentiment correlations

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Data Collection

API: Reddit API via requests and JSON parsing (no PRAW dependency)

Fields collected per post:

Field	Description
title	Post title
selftext	Post body content
upvote_ratio	Ratio of upvotes to total votes
ups	Total upvotes
downs	Total downvotes
stock_mentions	Tickers mentioned in post (regex extraction)

Data cleaned: null values removed, irrelevant columns dropped → exported to output.csv.

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Sentiment Analysis Pipeline

Reddit API (r/wallstreetbets)
        │
        ▼
Post Scraping (requests + JSON)
→ Raw posts: title, body, upvotes, downvotes
        │
        ▼
Text Preprocessing (nltk)
  ├── Tokenisation
  ├── Stop-word removal
  └── Lemmatisation
        │
        ▼
Sentiment Scoring (NLTK / VADER-style)
→ Score ∈ [−1, 1]
        │
        ▼
Signal Classification
  ├── Score > 0.5  → Buy
  ├── Score < −0.5 → Sell
  └── Otherwise    → Hold
        │
        ▼
Visualisation (matplotlib + seaborn)
→ 6 charts + sentiment_analysis_report.md

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Results & Visualisations

Summary Statistics

Metric	Value
Average Sentiment Score	0.08 (slightly positive overall)
Median Sentiment Score	0.00 (neutral median)
Buy signals	20 posts
Hold signals	70 posts
Sell signals	9 posts

Sentiment Score Over Time

High volatility in individual post scores — scores swing from −1 to +1 day-to-day. Red line marks neutral (0).

Sentiment Score Distribution

Bimodal distribution: a spike at 0 (neutral posts) and a secondary peak near +1. The community skews toward strongly positive sentiment, with a long left tail of strongly negative posts.

Score Range	Interpretation	Frequency
0.5 to 1.0	Strongly positive (Buy zone)	~20%
−0.1 to 0.1	Neutral	~45%
−1.0 to −0.5	Strongly negative (Sell zone)	~9%
All others	Mixed sentiment (Hold zone)	~26%

7-Day Rolling Average

Rolling average smooths noise and reveals regime shifts — sustained positive periods (bullish runs) followed by sharp negative dips correlating with market events.

Buy/Sell/Hold Distribution

Signal	Count	Share
Buy	20	~20%
Hold	70	~70%
Sell	9	~9%

70% of posts fall in the Hold zone — consistent with WSB's chaotic mix of DD posts, memes, and loss porn.

Sentiment by Recommendation (Box Plot)

Signal	Median Score	Score Range	Interpretation
Buy	~0.85	0.5 to 1.0	Tight cluster of strongly positive posts
Sell	~−0.90	−1.0 to −0.5	Tight cluster of strongly negative posts
Hold	~0.00	−0.5 to 0.5	Full spread — genuinely mixed community sentiment

Strong separation: Buy posts cluster at 0.8–1.0, Sell posts at −0.85 to −1.0, Hold posts cover the full range.

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Trading Signal Logic

if sentiment_score > 0.5:
    signal = "Buy"
elif sentiment_score < -0.5:
    signal = "Sell"
else:
    signal = "Hold"

Key finding: Positive sentiment reliably predicts Buy sentiment clusters; negative sentiment predicts Sell clusters. The Hold zone contains genuinely mixed/uncertain community sentiment.

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How to Run

git clone https://github.com/aguru-venkata-saisantosh-patnaik/Data_Analysis_on_wallstreetbets_using_redditAPI.git
cd Data_Analysis_on_wallstreetbets_using_redditAPI
pip install pandas numpy matplotlib seaborn nltk requests

Reddit API Setup:

1. Create a Reddit app at reddit.com/prefs/apps (script type)
2. Set client_id, client_secret, user_agent in the scraping notebook

Run notebooks in order:

1. reddit_wallstreetbets_scrapper.ipynb — scrape posts → output.csv
2. Visualisation and Report.ipynb — sentiment scoring, classification, and all charts

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Files

File	Description
reddit_wallstreetbets_scrapper.ipynb	Reddit API scraper → CSV
Visualisation and Report.ipynb	NLP analysis, signal classification, 6 visualisations
output.csv	Scraped and cleaned dataset
sentiment_analysis_report.md	Auto-generated summary report
Data_Analysis_on_wallstreetbets_using_redditAPI_Report.pdf	Full PDF report

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