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# Install necessary packages
import praw
import pandas as pd
import matplotlib.pyplot as plt
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import os
import gradio as gr
# Download the VADER lexicon
nltk.download('vader_lexicon', quiet=True)
class RedditSentimentAgent:
def __init__(self, client_id, client_secret, user_agent):
# Initialize VADER sentiment analyzer
self.sia = SentimentIntensityAnalyzer()
# Initialize Reddit API client
self.reddit = praw.Reddit(
client_id=client_id,
client_secret=client_secret,
user_agent=user_agent
)
def get_comments(self, subreddit_name, limit=1000):
subreddit = self.reddit.subreddit(subreddit_name)
comments = []
for comment in subreddit.comments(limit=limit):
comments.append(comment.body)
return comments
def analyze_sentiment(self, text):
return self.sia.polarity_scores(text)['compound']
def categorize_sentiment(self, score):
if score <= -0.05:
return 'Negative'
elif score >= 0.05:
return 'Positive'
else:
return 'Neutral'
def analyze_subreddit(self, subreddit_name, limit=1000):
comments = self.get_comments(subreddit_name, limit)
sentiments = [self.analyze_sentiment(comment) for comment in comments]
df = pd.DataFrame({'comment': comments, 'sentiment': sentiments})
df['sentiment_category'] = df['sentiment'].apply(self.categorize_sentiment)
return df
def visualize_results(self, df, subreddit_name):
# Histogram of sentiment scores
fig1, ax1 = plt.subplots(figsize=(10, 6))
ax1.hist(df['sentiment'], bins=20, edgecolor='black')
ax1.set_title(f'Sentiment Distribution in r/{subreddit_name} using VADER')
ax1.set_xlabel('Sentiment Polarity')
ax1.set_ylabel('Frequency')
# Bar chart of sentiment categories
sentiment_counts = df['sentiment_category'].value_counts()
fig2, ax2 = plt.subplots(figsize=(8, 6))
sentiment_counts.plot(kind='bar', ax=ax2)
ax2.set_title(f'Sentiment Distribution in r/{subreddit_name}')
ax2.set_xlabel('Sentiment Category')
ax2.set_ylabel('Number of Comments')
return fig1, fig2
def get_statistics(self, df):
stats = f"Average sentiment: {df['sentiment'].mean():.2f}\n"
stats += f"Median sentiment: {df['sentiment'].median():.2f}\n"
stats += f"Most positive comment: {df.loc[df['sentiment'].idxmax(), 'comment']}\n"
stats += f"Most negative comment: {df.loc[df['sentiment'].idxmin(), 'comment']}\n"
sentiment_counts = df['sentiment_category'].value_counts()
stats += f"\nSentiment Distribution:\n{sentiment_counts}"
return stats
def analyze(self, subreddit_name, limit):
try:
df = self.analyze_subreddit(subreddit_name, limit)
fig1, fig2 = self.visualize_results(df, subreddit_name)
stats = self.get_statistics(df)
return fig1, fig2, stats
except Exception as e:
return None, None, f"An error occurred: {str(e)}"
# Replace these with your actual Reddit API credentials
REDDIT_CLIENT_ID = "rDCXZ2gSgcW-GVwC01MlUw"
REDDIT_CLIENT_SECRET = "7_7u6nwF8Ok-il14ek2yt6rv3vg0gQ"
REDDIT_USER_AGENT = "IcyTruth4022"
agent = RedditSentimentAgent(REDDIT_CLIENT_ID, REDDIT_CLIENT_SECRET, REDDIT_USER_AGENT)
def gradio_interface(subreddit_name, limit):
return agent.analyze(subreddit_name, int(limit))
iface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.Textbox(label="Subreddit Name"),
gr.Slider(minimum=10, maximum=1000, step=10, label="Number of Comments", value=100)
],
outputs=[
gr.Plot(label="Sentiment Distribution"),
gr.Plot(label="Sentiment Categories"),
gr.Textbox(label="Statistics", lines=10)
],
title="Reddit Sentiment Analysis",
description="Analyze the sentiment of comments in a subreddit using VADER."
)
iface.launch() |