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README.md

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----
-title: Sentiment Analysis
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: A powerful web application for analyzing sentiment in text d
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

analyzer.py

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+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+class SentimentAnalyzer:
+    def __init__(self):
+        self.vader = SentimentIntensityAnalyzer()
+    
+    def analyze_textblob(self, text):
+        """Analyze text sentiment using TextBlob"""
+        analysis = TextBlob(text)
+        polarity = analysis.sentiment.polarity
+        
+        # sentiment category
+        if polarity > 0.05:
+            sentiment = "Positive"
+            emoji = "😃"
+        elif polarity < -0.05:
+            sentiment = "Negative"
+            emoji = "😞"
+        else:
+            sentiment = "Neutral"
+            emoji = "😐"
+        
+        return {
+            "sentiment": sentiment,
+            "polarity": polarity,
+            "emoji": emoji,
+            "subjectivity": analysis.sentiment.subjectivity
+        }
+    
+    def analyze_vader(self, text):
+        """Analyze text sentiment using VADER"""
+        scores = self.vader.polarity_scores(text)
+        
+        # determine sentiment category based on compound score
+        if scores["compound"] >= 0.05:
+            sentiment = "Positive"
+            emoji = "😃"
+        elif scores["compound"] <= -0.05:
+            sentiment = "Negative"
+            emoji = "😞"
+        else:
+            sentiment = "Neutral"
+            emoji = "😐"
+        
+        return {
+            "sentiment": sentiment,
+            "compound": scores["compound"],
+            "pos": scores["pos"],
+            "neu": scores["neu"],
+            "neg": scores["neg"],
+            "emoji": emoji
+        }

app.py

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+# Author: Jivan Jamdar
+# Date: 2023-10-01 Time: 20:57:43PM
+# Description: Streamlit app for sentiment analysis using TextBlob and VADER
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from analyzer import SentimentAnalyzer
+
+def main():
+    st.set_page_config(
+        page_title="Sentiment Analysis App",
+        page_icon="📊",
+        layout="wide"
+    )
+    
+    st.title("📊 Sentiment Analysis Tool")
+    st.write("Analyze the sentiment of tweets, product reviews, or any text!")
+    
+    analyzer = SentimentAnalyzer()
+    
+    # different tabs => different functionalities
+    tab1, tab2, tab3 = st.tabs(["Single Text Analysis", "Batch Analysis", "About"])
+    
+    with tab1:
+        st.subheader("Single Text Analysis")
+        
+        # tool selection
+        analysis_tool = st.radio(
+            "Select analysis tool:",
+            ["TextBlob", "VADER"],
+            horizontal=True
+        )
+        
+        # text input
+        text_input = st.text_area(
+            "Enter text to analyze:",
+            height=150,
+            placeholder="Type or paste your text here..."
+        )
+        
+        if st.button("Analyze Sentiment"):
+            if text_input:
+                with st.spinner("Analyzing..."):
+                    if analysis_tool == "TextBlob":
+                        result = analyzer.analyze_textblob(text_input)
+                        method = "TextBlob"
+                    else:
+                        result = analyzer.analyze_vader(text_input)
+                        method = "VADER"
+                
+                # display results with columns
+                st.subheader("Results")
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    st.markdown(f"### {result['emoji']} {result['sentiment']}")
+                    st.write(f"Analysis method: {method}")
+                    
+                with col2:
+                    if method == "TextBlob":
+                        st.metric("Polarity", f"{result['polarity']:.2f}")
+                        st.metric("Subjectivity", f"{result['subjectivity']:.2f}")
+                    else:  # VADER
+                        st.metric("Compound Score", f"{result['compound']:.2f}")
+                
+                # detailed breakdown for VADER
+                if method == "VADER":
+                    st.subheader("Sentiment Breakdown")
+                    vader_df = pd.DataFrame({
+                        'Sentiment': ['Positive', 'Neutral', 'Negative'],
+                        'Score': [result['pos'], result['neu'], result['neg']]
+                    })
+                    
+                    fig = px.bar(
+                        vader_df, 
+                        x='Sentiment', 
+                        y='Score',
+                        color='Sentiment',
+                        color_discrete_map={
+                            'Positive': '#2ECC71',
+                            'Neutral': '#3498DB',
+                            'Negative': '#E74C3C'
+                        }
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
+            else:
+                st.warning("Please enter some text to analyze.")
+    
+    with tab2:
+        st.subheader("Batch Analysis")
+        st.write("Upload a CSV or Excel file with a column containing text to analyze multiple entries at once.")
+        
+        uploaded_file = st.file_uploader("Upload your file", type=["csv", "xlsx"])
+        
+        if uploaded_file is not None:
+            try:
+                if uploaded_file.name.endswith('.csv'):
+                    df = pd.read_csv(uploaded_file)
+                else:
+                    df = pd.read_excel(uploaded_file)
+                
+                st.write("Preview of uploaded data:")
+                st.dataframe(df.head())
+                
+                text_column = st.selectbox("Select the column containing text to analyze:", df.columns)
+                analysis_tool = st.radio(
+                    "Select analysis tool for batch processing:",
+                    ["TextBlob", "VADER"],
+                    horizontal=True
+                )
+                
+                if st.button("Run Batch Analysis"):
+                    with st.spinner("Analyzing all entries..."):
+                        results = []
+                        
+                        for text in df[text_column]:
+                            if pd.notna(text):  # skip NaN values
+                                if analysis_tool == "TextBlob":
+                                    result = analyzer.analyze_textblob(str(text))
+                                else:
+                                    result = analyzer.analyze_vader(str(text))
+                                results.append(result)
+                            else:
+                                # handle NaN values
+                                results.append({
+                                    'sentiment': 'Unknown',
+                                    'emoji': '❓',
+                                    'compound' if analysis_tool == "VADER" else 'polarity': 0
+                                })
+                        
+                        # create results DataFrame
+                        results_df = pd.DataFrame(results)
+                        df_with_sentiment = pd.concat([df, results_df], axis=1)
+                        
+                        st.subheader("Results")
+                        st.dataframe(df_with_sentiment)
+                        
+                        # download button for results
+                        st.download_button(
+                            label="Download Results",
+                            data=df_with_sentiment.to_csv(index=False),
+                            file_name="sentiment_analysis_results.csv",
+                            mime="text/csv"
+                        )
+                        
+                        # show summary statistics
+                        st.subheader("Sentiment Distribution")
+                        sentiment_counts = results_df['sentiment'].value_counts().reset_index()
+                        sentiment_counts.columns = ['Sentiment', 'Count']
+                        
+                        fig = px.pie(
+                            sentiment_counts, 
+                            names='Sentiment', 
+                            values='Count',
+                            color='Sentiment',
+                            color_discrete_map={
+                                'Positive': '#2ECC71',
+                                'Neutral': '#3498DB',
+                                'Negative': '#E74C3C',
+                                'Unknown': '#95A5A6'
+                            }
+                        )
+                        st.plotly_chart(fig, use_container_width=True)
+                
+            except Exception as e:
+                st.error(f"Error processing the uploaded file: {str(e)}")
+    
+    with tab3:
+        st.subheader("About This App")
+        st.write("""
+        This Sentiment Analysis App uses two popular techniques:
+        
+        1. **TextBlob**: A simple NLP library that provides a simple API for diving into common NLP tasks.
+           - Polarity: Score from -1 (very negative) to +1 (very positive)
+           - Subjectivity: Score from 0 (objective) to 1 (subjective)
+        
+        2. **VADER** (Valence Aware Dictionary and sEntiment Reasoner): A lexicon and rule-based sentiment analysis tool specifically attuned to sentiments expressed in social media.
+           - Compound: Normalized score from -1 (very negative) to +1 (very positive)
+           - Positive, Neutral, Negative: Proportions of text that fall in each category
+        
+        ### When to use each tool:
+        - TextBlob is simpler and works well for general text
+        - VADER is better for social media content, slang, and emoticons
+        
+        ### Limitations:
+        - Neither tool understands sarcasm well
+        - Context is often missed
+        - Language specific (works best with English)
+        """)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit>=1.24.0
+textblob>=0.17.1
+vaderSentiment>=3.3.2
+pandas>=2.0.3
+matplotlib==3.7.2
+plotly>=5.15.0