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| git add app.py requirements.txt | |
| git commit -m "Initial commit" | |
| git push | |
| import streamlit as st | |
| import nltk | |
| from nltk.sentiment.vader import SentimentIntensityAnalyzer | |
| # Ensure the VADER lexicon is downloaded | |
| nltk.download('vader_lexicon') | |
| class SentimentAnalyzer: | |
| def __init__(self): | |
| self.analyzer = SentimentIntensityAnalyzer() | |
| def analyze_sentiment(self, sentence): | |
| return self.analyzer.polarity_scores(sentence) | |
| def fool(): | |
| analyzer = SentimentAnalyzer() | |
| st.title("Sentiment Analysis App using VADER") | |
| st.write("Enter a sentence to analyze its sentiment:") | |
| # Input text box for user input | |
| sentence = st.text_input("Input sentence:") | |
| if st.button("Analyze"): | |
| if sentence: | |
| # Perform sentiment analysis | |
| result = analyzer.analyze_sentiment(sentence) | |
| # Interpret sentiment label | |
| compound_score = result['compound'] | |
| if compound_score >= 0.05: | |
| sentiment_type = 'Positive' | |
| elif compound_score <= -0.05: | |
| sentiment_type = 'Negative' | |
| else: | |
| sentiment_type = 'Neutral' | |
| # Display sentiment analysis result | |
| st.write(f"Sentiment: {sentiment_type}, Score: {compound_score:.4f}") | |
| # Call fool function directly if the script is executed | |
| fool() | |