Sentiment-Analysis-using-Machine-Learning/main.py at main · tushargautam681/Sentiment-Analysis-using-Machine-Learning · GitHub

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import streamlit as st
import pandas as pd
from textblob import TextBlob
import io

st.set_page_config(
    page_title="Sentiment Analysis App",
    page_icon="📊",
    layout="wide"
)

def analyze_sentiment(text):
    """Analyze sentiment of a given text using TextBlob"""
    try:
        blob = TextBlob(str(text))
        polarity = blob.sentiment.polarity
        subjectivity = blob.sentiment.subjectivity

        # Classify sentiment based on polarity
        if polarity > 0.1:
            sentiment_label = "Positive"
        elif polarity < -0.1:
            sentiment_label = "Negative"
        else:
            sentiment_label = "Neutral"

        return polarity, subjectivity, sentiment_label
    except:
        return 0.0, 0.0, "Neutral"

def main():
    st.title("📊 Sentiment Analysis Tool")
    st.markdown("Upload a CSV file to analyze sentiment and add sentiment values to your data.")

    # File uploader
    uploaded_file = st.file_uploader("Choose a CSV file", type=['csv'])

    if uploaded_file is not None:
        try:
            # Read CSV file
            df = pd.read_csv(uploaded_file)

            st.success(f"✅ File loaded successfully! Found {len(df)} rows.")

            # Display column names
            st.subheader("📋 File Columns")
            st.write(df.columns.tolist())

            # Column selection
            text_column = st.selectbox(
                "Select the column containing text to analyze:",
                options=df.columns.tolist(),
                index=0
            )

            if st.button("🔍 Analyze Sentiment"):
                with st.spinner("Analyzing sentiment... This may take a moment."):
                    # Analyze sentiment for each row
                    results = df[text_column].apply(analyze_sentiment)

                    # Add sentiment columns to dataframe
                    df['polarity'] = [r[0] for r in results]
                    df['subjectivity'] = [r[1] for r in results]
                    df['sentiment'] = [r[2] for r in results]

                    st.success("✅ Sentiment analysis complete!")

                    # Display results
                    st.subheader("📈 Analysis Results")
                    st.dataframe(df, use_container_width=True)

                    # Statistics
                    col1, col2, col3, col4 = st.columns(4)

                    with col1:
                        st.metric("Total Rows", len(df))

                    with col2:
                        positive_count = len(df[df['sentiment'] == 'Positive'])
                        st.metric("Positive", positive_count)

                    with col3:
                        negative_count = len(df[df['sentiment'] == 'Negative'])
                        st.metric("Negative", negative_count)

                    with col4:
                        neutral_count = len(df[df['sentiment'] == 'Neutral'])
                        st.metric("Neutral", neutral_count)

                    # Download button
                    csv_buffer = io.StringIO()
                    df.to_csv(csv_buffer, index=False)
                    csv_data = csv_buffer.getvalue()

                    st.download_button(
                        label="📥 Download CSV with Sentiment Analysis",
                        data=csv_data,
                        file_name="sentiment_analysis_results.csv",
                        mime="text/csv"
                    )

                    # Store in session state for later use
                    st.session_state['analyzed_df'] = df

        except Exception as e:
            st.error(f"❌ Error processing file: {str(e)}")
            st.info("Please make sure your CSV file is properly formatted.")

    else:
        st.info("👆 Please upload a CSV file to get started.")

        # Sample CSV format
        st.subheader("📝 Expected CSV Format")
        sample_data = {
            'id': [1, 2, 3],
            'text': [
                'I love this product! It is amazing.',
                'This is terrible. Very disappointed.',
                'It is okay, nothing special.'
            ]
        }
        sample_df = pd.DataFrame(sample_data)
        st.dataframe(sample_df)
        st.caption("Your CSV should contain at least one column with text data to analyze.")

if __name__ == "__main__":
    main()