Spaces:

kraghavendra99
/

TwitterSentimentAnalysis

Sleeping

App Files Files Community

TwitterSentimentAnalysis / app.py

kraghavendra99

update

2152c65 verified 7 months ago

Raw History Blame Contribute Delete

10.3 kB

import gradio as gr
import re
import pickle
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import TweetTokenizer

# Download required NLTK data
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

try:
    nltk.data.find('corpora/stopwords')
except LookupError:
    nltk.download('stopwords')

try:
    nltk.data.find('corpora/wordnet')
except LookupError:
    nltk.download('wordnet')

# Initialize tokenizer and lemmatizer
tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True, reduce_len=True)
lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words("english"))

def preprocess_tweet(tweet):
    """
    Preprocess a tweet by cleaning and normalizing the text.
    """
    # Remove URLs
    tweet = re.sub(r"http\S+|www\S+|https\S+", "", tweet, flags=re.MULTILINE)
    # Remove punctuation and numbers
    tweet = re.sub(r"[^a-zA-Z]", " ", tweet)  # Keep only letters
    
    # Tokenize
    tokens = tokenizer.tokenize(tweet)
    
    # Remove stopwords and lemmatize
    processed_tokens = []
    for word in tokens:
        if word not in stop_words and len(word) > 2:  # Also filter out very short words
            processed_tokens.append(lemmatizer.lemmatize(word))
            
    return " ".join(processed_tokens)

# Create a simple demo model (in a real scenario, you'd load a pre-trained model)
class SentimentAnalyzer:
    def __init__(self):
        self.vectorizer = None
        self.model = None
        self.is_trained = False
        
    def create_demo_model(self):
        """
        Create a simple demo model with sample data for demonstration purposes.
        In a real application, you would load your trained model here.
        """
        # Sample training data for demo purposes
        sample_tweets = [
            "I love this product! It's amazing!",
            "This is the worst thing ever.",
            "Great service and friendly staff!",
            "Terrible experience, would not recommend.",
            "Amazing quality and fast delivery!",
            "Poor customer service and bad quality.",
            "Excellent work, very satisfied!",
            "Disappointing results, not worth it.",
            "Outstanding performance, highly recommend!",
            "Awful experience, complete waste of money.",
            "Fantastic product, exceeded expectations!",
            "Horrible quality, broke immediately.",
            "Wonderful experience, will buy again!",
            "Terrible product, don't waste your money.",
            "Incredible service, very professional!",
            "Worst purchase ever made.",
            "Perfect solution for my needs!",
            "Completely useless and overpriced.",
            "Brilliant design and functionality!",
            "Absolutely terrible, avoid at all costs."
        ]
        
        sample_labels = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]  # 1 = positive, 0 = negative
        
        # Preprocess the sample tweets
        processed_tweets = [preprocess_tweet(tweet) for tweet in sample_tweets]
        
        # Create and train the model
        self.vectorizer = TfidfVectorizer(max_features=1000, min_df=1, max_df=0.8)
        X = self.vectorizer.fit_transform(processed_tweets)
        
        self.model = MultinomialNB()
        self.model.fit(X, sample_labels)
        self.is_trained = True
        
    def predict_sentiment(self, text):
        """
        Predict the sentiment of a given text.
        """
        if not self.is_trained:
            self.create_demo_model()
            
        # Preprocess the input text
        processed_text = preprocess_tweet(text)
        
        if not processed_text.strip():
            return "Neutral", 0.5, "⚪"
        
        # Transform the text using the fitted vectorizer
        text_features = self.vectorizer.transform([processed_text])
        
        # Make prediction
        prediction = self.model.predict(text_features)[0]
        prediction_proba = self.model.predict_proba(text_features)[0]
        
        # Get confidence score
        confidence = max(prediction_proba)
        
        # Determine sentiment and emoji
        if prediction == 1:
            sentiment = "Positive"
            emoji = "😊"
        else:
            sentiment = "Negative"
            emoji = "😞"
            
        return sentiment, confidence, emoji

# Initialize the sentiment analyzer
analyzer = SentimentAnalyzer()

def analyze_sentiment(text):
    """
    Main function to analyze sentiment that will be called by Gradio.
    """
    if not text.strip():
        return "Please enter some text to analyze.", "", ""
    
    sentiment, confidence, emoji = analyzer.predict_sentiment(text)
    
    # Format the results
    result = f"{emoji} **{sentiment}**"
    confidence_text = f"Confidence: {confidence:.2%}"
    
    # Add interpretation
    if confidence > 0.8:
        interpretation = "High confidence prediction"
    elif confidence > 0.6:
        interpretation = "Moderate confidence prediction"
    else:
        interpretation = "Low confidence prediction"
    
    return result, confidence_text, interpretation

# Example tweets for testing
example_tweets = [
    "I absolutely love this new phone! The camera quality is amazing and the battery lasts all day.",
    "This movie was terrible. Waste of time and money. Would not recommend to anyone.",
    "The weather is nice today.",
    "Just had the worst customer service experience ever. So frustrated right now!",
    "Thank you so much for your help! You made my day better.",
    "This restaurant has the best food in town! Highly recommend the pasta.",
    "I'm so disappointed with this purchase. It broke after just one day.",
    "Great job on the presentation! Very informative and well organized.",
    "Traffic is horrible today. Going to be late for my meeting.",
    "Excited to start my new job next week! Can't wait to meet the team."
]

# Create the Gradio interface
with gr.Blocks(
    theme=gr.themes.Soft(),
    title="Twitter Sentiment Analysis",
    css="""
    .main-header {
        text-align: center;
        margin-bottom: 2rem;
    }
    .result-box {
        padding: 1rem;
        border-radius: 8px;
        margin: 1rem 0;
    }
    .positive {
        background-color: #d4edda;
        border: 1px solid #c3e6cb;
    }
    .negative {
        background-color: #f8d7da;
        border: 1px solid #f5c6cb;
    }
    """
) as demo:
    
    # Header
    gr.Markdown(
        """
        # 🐦 Twitter Sentiment Analysis
        
        ### Analyze the sentiment of tweets and text using machine learning
        
        This tool uses a Naive Bayes classifier trained on tweet data to determine whether text expresses positive or negative sentiment. 
        Simply enter your text below and click "Analyze Sentiment" to get started!
        """,
        elem_classes=["main-header"]
    )
    
    with gr.Row():
        with gr.Column(scale=2):
            # Input section
            text_input = gr.Textbox(
                label="Enter text to analyze",
                placeholder="Type your tweet or text here...",
                lines=4,
                max_lines=10
            )
            
            with gr.Row():
                analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
        
        with gr.Column(scale=1):
            # Output section
            sentiment_output = gr.Markdown(label="Sentiment", value="")
            confidence_output = gr.Markdown(label="Confidence", value="")
            interpretation_output = gr.Markdown(label="Interpretation", value="")
    
    # Examples section
    gr.Markdown("### 📝 Try these examples:")
    
    with gr.Row():
        example_buttons = []
        for i in range(0, min(6, len(example_tweets)), 2):
            with gr.Column():
                for j in range(2):
                    if i + j < len(example_tweets):
                        btn = gr.Button(
                            example_tweets[i + j][:50] + "..." if len(example_tweets[i + j]) > 50 else example_tweets[i + j],
                            size="sm"
                        )
                        btn.click(
                            lambda x=example_tweets[i + j]: x,
                            outputs=text_input
                        )
    
    # Model information
    with gr.Accordion("ℹ️ About this model", open=False):
        gr.Markdown(
            """
            **Model Details:**
            - **Algorithm**: Multinomial Naive Bayes
            - **Features**: TF-IDF (Term Frequency-Inverse Document Frequency)
            - **Preprocessing**: Text cleaning, tokenization, stopword removal, lemmatization
            - **Classes**: Positive, Negative
            
            **Note**: This is a demonstration model trained on a small sample dataset. 
            For production use, you would want to train on a larger, more diverse dataset like the Sentiment140 dataset.
            
            **Limitations**:
            - May not handle sarcasm or complex sentiment well
            - Performance depends on similarity to training data
            - Neutral sentiments may be classified as positive or negative
            """
        )
    
    # Event handlers
    analyze_btn.click(
        fn=analyze_sentiment,
        inputs=text_input,
        outputs=[sentiment_output, confidence_output, interpretation_output]
    )
    
    clear_btn.click(
        lambda: ("", "", "", ""),
        outputs=[text_input, sentiment_output, confidence_output, interpretation_output]
    )
    
    # Auto-analyze on text change (with debounce)
    text_input.change(
        fn=analyze_sentiment,
        inputs=text_input,
        outputs=[sentiment_output, confidence_output, interpretation_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()

	import gradio as gr
	import re
	import pickle
	import pandas as pd
	from sklearn.feature_extraction.text import TfidfVectorizer
	from sklearn.naive_bayes import MultinomialNB
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import accuracy_score, classification_report
	import nltk
	from nltk.corpus import stopwords
	from nltk.stem import WordNetLemmatizer
	from nltk.tokenize import TweetTokenizer

	# Download required NLTK data
	try:
	nltk.data.find('tokenizers/punkt')
	except LookupError:
	nltk.download('punkt')

	try:
	nltk.data.find('corpora/stopwords')
	except LookupError:
	nltk.download('stopwords')

	try:
	nltk.data.find('corpora/wordnet')
	except LookupError:
	nltk.download('wordnet')

	# Initialize tokenizer and lemmatizer
	tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True, reduce_len=True)
	lemmatizer = WordNetLemmatizer()
	stop_words = set(stopwords.words("english"))

	def preprocess_tweet(tweet):
	"""
	Preprocess a tweet by cleaning and normalizing the text.
	"""
	# Remove URLs
	tweet = re.sub(r"http\S+\|www\S+\|https\S+", "", tweet, flags=re.MULTILINE)
	# Remove punctuation and numbers
	tweet = re.sub(r"[^a-zA-Z]", " ", tweet) # Keep only letters

	# Tokenize
	tokens = tokenizer.tokenize(tweet)

	# Remove stopwords and lemmatize
	processed_tokens = []
	for word in tokens:
	if word not in stop_words and len(word) > 2: # Also filter out very short words
	processed_tokens.append(lemmatizer.lemmatize(word))

	return " ".join(processed_tokens)

	# Create a simple demo model (in a real scenario, you'd load a pre-trained model)
	class SentimentAnalyzer:
	def __init__(self):
	self.vectorizer = None
	self.model = None
	self.is_trained = False

	def create_demo_model(self):
	"""
	Create a simple demo model with sample data for demonstration purposes.
	In a real application, you would load your trained model here.
	"""
	# Sample training data for demo purposes
	sample_tweets = [
	"I love this product! It's amazing!",
	"This is the worst thing ever.",
	"Great service and friendly staff!",
	"Terrible experience, would not recommend.",
	"Amazing quality and fast delivery!",
	"Poor customer service and bad quality.",
	"Excellent work, very satisfied!",
	"Disappointing results, not worth it.",
	"Outstanding performance, highly recommend!",
	"Awful experience, complete waste of money.",
	"Fantastic product, exceeded expectations!",
	"Horrible quality, broke immediately.",
	"Wonderful experience, will buy again!",
	"Terrible product, don't waste your money.",
	"Incredible service, very professional!",
	"Worst purchase ever made.",
	"Perfect solution for my needs!",
	"Completely useless and overpriced.",
	"Brilliant design and functionality!",
	"Absolutely terrible, avoid at all costs."
	]

	sample_labels = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] # 1 = positive, 0 = negative

	# Preprocess the sample tweets
	processed_tweets = [preprocess_tweet(tweet) for tweet in sample_tweets]

	# Create and train the model
	self.vectorizer = TfidfVectorizer(max_features=1000, min_df=1, max_df=0.8)
	X = self.vectorizer.fit_transform(processed_tweets)

	self.model = MultinomialNB()
	self.model.fit(X, sample_labels)
	self.is_trained = True

	def predict_sentiment(self, text):
	"""
	Predict the sentiment of a given text.
	"""
	if not self.is_trained:
	self.create_demo_model()

	# Preprocess the input text
	processed_text = preprocess_tweet(text)

	if not processed_text.strip():
	return "Neutral", 0.5, "⚪"

	# Transform the text using the fitted vectorizer
	text_features = self.vectorizer.transform([processed_text])

	# Make prediction
	prediction = self.model.predict(text_features)[0]
	prediction_proba = self.model.predict_proba(text_features)[0]

	# Get confidence score
	confidence = max(prediction_proba)

	# Determine sentiment and emoji
	if prediction == 1:
	sentiment = "Positive"
	emoji = "😊"
	else:
	sentiment = "Negative"
	emoji = "😞"

	return sentiment, confidence, emoji

	# Initialize the sentiment analyzer
	analyzer = SentimentAnalyzer()

	def analyze_sentiment(text):
	"""
	Main function to analyze sentiment that will be called by Gradio.
	"""
	if not text.strip():
	return "Please enter some text to analyze.", "", ""

	sentiment, confidence, emoji = analyzer.predict_sentiment(text)

	# Format the results
	result = f"{emoji} {sentiment}"
	confidence_text = f"Confidence: {confidence:.2%}"

	# Add interpretation
	if confidence > 0.8:
	interpretation = "High confidence prediction"
	elif confidence > 0.6:
	interpretation = "Moderate confidence prediction"
	else:
	interpretation = "Low confidence prediction"

	return result, confidence_text, interpretation

	# Example tweets for testing
	example_tweets = [
	"I absolutely love this new phone! The camera quality is amazing and the battery lasts all day.",
	"This movie was terrible. Waste of time and money. Would not recommend to anyone.",
	"The weather is nice today.",
	"Just had the worst customer service experience ever. So frustrated right now!",
	"Thank you so much for your help! You made my day better.",
	"This restaurant has the best food in town! Highly recommend the pasta.",
	"I'm so disappointed with this purchase. It broke after just one day.",
	"Great job on the presentation! Very informative and well organized.",
	"Traffic is horrible today. Going to be late for my meeting.",
	"Excited to start my new job next week! Can't wait to meet the team."
	]

	# Create the Gradio interface
	with gr.Blocks(
	theme=gr.themes.Soft(),
	title="Twitter Sentiment Analysis",
	css="""
	.main-header {
	text-align: center;
	margin-bottom: 2rem;
	}
	.result-box {
	padding: 1rem;
	border-radius: 8px;
	margin: 1rem 0;
	}
	.positive {
	background-color: #d4edda;
	border: 1px solid #c3e6cb;
	}
	.negative {
	background-color: #f8d7da;
	border: 1px solid #f5c6cb;
	}
	"""
	) as demo:

	# Header
	gr.Markdown(
	"""
	# 🐦 Twitter Sentiment Analysis

	### Analyze the sentiment of tweets and text using machine learning

	This tool uses a Naive Bayes classifier trained on tweet data to determine whether text expresses positive or negative sentiment.
	Simply enter your text below and click "Analyze Sentiment" to get started!
	""",
	elem_classes=["main-header"]
	)

	with gr.Row():
	with gr.Column(scale=2):
	# Input section
	text_input = gr.Textbox(
	label="Enter text to analyze",
	placeholder="Type your tweet or text here...",
	lines=4,
	max_lines=10
	)

	with gr.Row():
	analyze_btn = gr.Button("🔍 Analyze Sentiment", variant="primary", size="lg")
	clear_btn = gr.Button("🗑️ Clear", variant="secondary")

	with gr.Column(scale=1):
	# Output section
	sentiment_output = gr.Markdown(label="Sentiment", value="")
	confidence_output = gr.Markdown(label="Confidence", value="")
	interpretation_output = gr.Markdown(label="Interpretation", value="")

	# Examples section
	gr.Markdown("### 📝 Try these examples:")

	with gr.Row():
	example_buttons = []
	for i in range(0, min(6, len(example_tweets)), 2):
	with gr.Column():
	for j in range(2):
	if i + j < len(example_tweets):
	btn = gr.Button(
	example_tweets[i + j][:50] + "..." if len(example_tweets[i + j]) > 50 else example_tweets[i + j],
	size="sm"
	)
	btn.click(
	lambda x=example_tweets[i + j]: x,
	outputs=text_input
	)

	# Model information
	with gr.Accordion("ℹ️ About this model", open=False):
	gr.Markdown(
	"""
	Model Details:
	- Algorithm: Multinomial Naive Bayes
	- Features: TF-IDF (Term Frequency-Inverse Document Frequency)
	- Preprocessing: Text cleaning, tokenization, stopword removal, lemmatization
	- Classes: Positive, Negative

	Note: This is a demonstration model trained on a small sample dataset.
	For production use, you would want to train on a larger, more diverse dataset like the Sentiment140 dataset.

	Limitations:
	- May not handle sarcasm or complex sentiment well
	- Performance depends on similarity to training data
	- Neutral sentiments may be classified as positive or negative
	"""
	)

	# Event handlers
	analyze_btn.click(
	fn=analyze_sentiment,
	inputs=text_input,
	outputs=[sentiment_output, confidence_output, interpretation_output]
	)

	clear_btn.click(
	lambda: ("", "", "", ""),
	outputs=[text_input, sentiment_output, confidence_output, interpretation_output]
	)

	# Auto-analyze on text change (with debounce)
	text_input.change(
	fn=analyze_sentiment,
	inputs=text_input,
	outputs=[sentiment_output, confidence_output, interpretation_output]
	)

	# Launch the app
	if __name__ == "__main__":
	demo.launch()