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FinalProject-1.py
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FinalProject-1.py
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# importing all necessary libraries
import praw
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import re
import matplotlib.pyplot as plt
from gensim import corpora, models
from transformers import pipeline
# Downloading NLTK Vader Lexicon (Sentiment Analysis)
nltk.download('vader_lexicon')
# Initializing Reddit API with your credentials
reddit = praw.Reddit(client_id='dhdghdx',
client_secret='yfyfjmfu',
user_agent='SocialMediaUnrest') #Replace the credentials
# Defining the subreddits related to your case study
subreddits = ['BlackLivesMatter', 'stopasianhate', 'immigration']
# Initializing SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
# Using Function for text preprocessing
def preprocess_text(text):
text = re.sub(r'http\S+', '', text)
text = re.sub(r'[^\w\s]', '', text)
return text
# Initializing variables for aggregated sentiment scores
total_positive = 0
total_negative = 0
total_neutral = 0
total_comments = 0
cleaned_comments = []
# Initializing BERT for sentiment analysis
nlp = pipeline("sentiment-analysis")
for subreddit_name in subreddits:
subreddit = reddit.subreddit(subreddit_name)
print(f"Top posts from r/{subreddit_name}:")
for submission in subreddit.top(limit=25):
print(f"Title: {submission.title}")
print(f"Score: {submission.score}")
print(f"Comments: {submission.num_comments}")
print(f"URL: {submission.url}")
print("\n")
submission.comments.replace_more(limit=None)
comments = submission.comments.list()
print("\nComments and their sentiments:")
for comment in comments[:10]:
comment_text = comment.body
comment_text = preprocess_text(comment_text)
cleaned_comments.append(comment_text)
# Sentiment analysis using NLTK Vader
sentiment = sia.polarity_scores(comment_text)
# Aggregating sentiment scores
if sentiment['compound'] >= 0.05:
total_positive += 1
elif sentiment['compound'] <= -0.05:
total_negative += 1
else:
total_neutral += 1
total_comments += 1
# Calculate percentages of sentiments
percentage_positive = (total_positive / total_comments) * 100
percentage_negative = (total_negative / total_comments) * 100
percentage_neutral = (total_neutral / total_comments) * 100
# Print aggregated sentiment results
print(f"Total Comments Analyzed: {total_comments}")
print(f"Percentage of Positive Comments: {percentage_positive:.2f}%")
print(f"Percentage of Negative Comments: {percentage_negative:.2f}%")
print(f"Percentage of Neutral Comments: {percentage_neutral:.2f}%")
# Perform BERT sentiment analysis
bert_sentiments = nlp(cleaned_comments)
# Visualizing sentiments
sentiment_labels = ['Positive', 'Neutral', 'Negative']
sentiment_percentages = [percentage_positive, percentage_neutral, percentage_negative]
plt.bar(sentiment_labels, sentiment_percentages)
plt.xlabel('Sentiment')
plt.ylabel('Percentage')
plt.title('Sentiment Distribution')
plt.show()
# Prepare text for topic modeling
text_data = [comment.split() for comment in cleaned_comments]
# Create a dictionary from the text data
dictionary = corpora.Dictionary(text_data)
corpus = [dictionary.doc2bow(text) for text in text_data]
# Perform LDA topic modeling
lda_model = models.LdaModel(corpus, num_topics=5, id2word=dictionary)