Python Reddit Analysis Tutorial: From Data Collection to Insights

# Install required packages
pip install praw pandas numpy matplotlib seaborn
pip install textblob nltk wordcloud
pip install scikit-learn

import praw
import pandas as pd
from datetime import datetime

# Initialize Reddit client
reddit = praw.Reddit(
    client_id='YOUR_CLIENT_ID',
    client_secret='YOUR_CLIENT_SECRET',
    user_agent='RedditAnalysis/1.0'
)

def collect_posts(subreddit_name, query, limit=500):
    """Collect posts matching query from subreddit"""
    subreddit = reddit.subreddit(subreddit_name)
    posts_data = []

    for post in subreddit.search(query, limit=limit):
        posts_data.append({
            'id': post.id,
            'title': post.title,
            'selftext': post.selftext,
            'score': post.score,
            'upvote_ratio': post.upvote_ratio,
            'num_comments': post.num_comments,
            'created_utc': datetime.fromtimestamp(post.created_utc),
            'author': str(post.author),
            'url': post.url
        })

    return pd.DataFrame(posts_data)

# Collect data
df = collect_posts('technology', 'artificial intelligence')
print(f"Collected {len(df)} posts")

import re

def clean_text(text):
    """Clean Reddit post text"""
    if pd.isna(text):
        return ""

    # Remove URLs
    text = re.sub(r'http\S+', '', text)
    # Remove special characters
    text = re.sub(r'[^\w\s]', '', text)
    # Lowercase
    text = text.lower()
    # Remove extra whitespace
    text = ' '.join(text.split())

    return text

# Apply cleaning
df['clean_title'] = df['title'].apply(clean_text)
df['clean_text'] = df['selftext'].apply(clean_text)

# Combine for analysis
df['full_text'] = df['clean_title'] + ' ' + df['clean_text']

from textblob import TextBlob

def get_sentiment(text):
    """Calculate sentiment polarity (-1 to 1)"""
    if not text:
        return 0
    blob = TextBlob(text)
    return blob.sentiment.polarity

def categorize_sentiment(score):
    """Convert score to category"""
    if score > 0.1:
        return 'positive'
    elif score < -0.1:
        return 'negative'
    else:
        return 'neutral'

# Apply sentiment analysis
df['sentiment_score'] = df['full_text'].apply(get_sentiment)
df['sentiment'] = df['sentiment_score'].apply(categorize_sentiment)

# View distribution
print(df['sentiment'].value_counts())

import matplotlib.pyplot as plt
import seaborn as sns

# Sentiment distribution
plt.figure(figsize=(10, 6))
sns.countplot(data=df, x='sentiment', palette='viridis')
plt.title('Sentiment Distribution in Reddit Posts')
plt.savefig('sentiment_dist.png')

# Sentiment over time
df['date'] = df['created_utc'].dt.date
daily_sentiment = df.groupby('date')['sentiment_score'].mean()

plt.figure(figsize=(12, 6))
daily_sentiment.plot(kind='line')
plt.title('Average Sentiment Over Time')
plt.savefig('sentiment_time.png')