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Twitter.py
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Twitter.py
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# single_sentiment_analysis.py
import pandas as pd
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
import matplotlib.pyplot as plt
import seaborn as sns
# Download NLTK data (if not already downloaded)
nltk.download("vader_lexicon")
def analyze_sentiment(tweet):
sia = SentimentIntensityAnalyzer()
polarity = sia.polarity_scores(tweet)["compound"]
# Determine sentiment label based on polarity
if polarity > 0.05:
return "Positive"
elif polarity < -0.05:
return "Negative"
else:
return "Neutral"
def analyze_dataset(csv):
df = pd.read_csv(csv, header=None)
df["sentiment"] = df[5].apply(analyze_sentiment) # Assuming tweet text is in the 6th column (index 5)
return df
if __name__ == "__main__":
csv_file = "C:/Users/Abhi/Downloads/testdata.manual.2009.06.14.csv" # Replace with the correct CSV file path
df = analyze_dataset(csv_file)
# Calculate the count of each sentiment
sentiment_counts = df["sentiment"].value_counts()
# Plot the sentiment distribution using a bar plot
plt.figure(figsize=(8, 6))
sns.barplot(x=sentiment_counts.index, y=sentiment_counts.values, palette="pastel")
plt.xlabel("Sentiment")
plt.ylabel("Count")
plt.title("Sentiment Analysis Results")
plt.show()