Reddit Data Analysis with Python 📊

Analyze Reddit posts, comments, and trends using Python. No Reddit API key needed — uses Reddit's public JSON endpoints.

What You Can Do

• Sentiment Analysis — Track positive/negative sentiment in any subreddit
• Trend Detection — Find emerging topics before they go viral
• Keyword Monitoring — Track mentions of your brand, product, or competitors
• Community Analysis — Understand subreddit demographics and posting patterns

Quick Start

pip install requests pandas textblob
python reddit_analyzer.py --subreddit startup --limit 100

How It Works

Reddit exposes a JSON API at reddit.com/r/{subreddit}.json. No authentication needed for public data.

import requests

def get_subreddit_posts(subreddit, limit=25):
    """Fetch posts from any subreddit — no API key needed."""
    url = f"https://www.reddit.com/r/{subreddit}/hot.json?limit={limit}"
    headers = {"User-Agent": "RedditAnalyzer/1.0"}
    response = requests.get(url, headers=headers)
    posts = response.json()["data"]["children"]
    return [{"title": p["data"]["title"],
             "score": p["data"]["score"],
             "comments": p["data"]["num_comments"],
             "author": p["data"]["author"],
             "url": p["data"]["url"]} for p in posts]

# Example: get hot posts from r/startup
posts = get_subreddit_posts("startup", limit=50)
for post in posts[:5]:
    print(f"{post[\"score\"]:>5} | {post[\"title\"][:60]}")

Sentiment Analysis

from textblob import TextBlob

def analyze_sentiment(posts):
    """Analyze sentiment of Reddit posts."""
    for post in posts:
        blob = TextBlob(post["title"])
        post["sentiment"] = blob.sentiment.polarity
        post["label"] = "positive" if post["sentiment"] > 0.1 else \
                        "negative" if post["sentiment"] < -0.1 else "neutral"
    return posts

results = analyze_sentiment(posts)
positive = sum(1 for r in results if r["label"] == "positive")
negative = sum(1 for r in results if r["label"] == "negative")
neutral = sum(1 for r in results if r["label"] == "neutral")
print(f"Positive: {positive} | Negative: {negative} | Neutral: {neutral}")

Keyword Tracking

def track_keyword(subreddit, keyword, limit=100):
    """Find posts mentioning a specific keyword."""
    url = f"https://www.reddit.com/r/{subreddit}/search.json?q={keyword}&restrict_sr=1&limit={limit}&sort=new"
    headers = {"User-Agent": "RedditAnalyzer/1.0"}
    response = requests.get(url, headers=headers)
    return response.json()["data"]["children"]

# Track "Claude AI" mentions in r/artificial
mentions = track_keyword("artificial", "Claude AI", limit=50)
print(f"Found {len(mentions)} mentions")

Scale with Apify

For production use (thousands of posts, scheduled runs, data storage):

🔗 Reddit Scraper on Apify — Cloud-hosted, handles rate limits, exports to JSON/CSV/Excel.

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Need Custom Data Analysis?

I build data extraction and analysis pipelines for any platform.

Hire me → | Email: Spinov001@gmail.com

License

MIT