Nlp Wizard Pro

I'll provide a comprehensive guide for implementing a real-time social media sentiment analysis system, tailored for beginners. 1. Project Overview & Architecture Let's design a straightforward but scalable system for monitoring social media sentiment in real-time. High-level System Design: - Data Collection Layer: Streams social media data - Processing Layer: Performs sentiment analysis - Storage Layer: Saves results - API Layer: Provides access to results Let me create a clear architectural diagram using Mermaid. ```mermaid flowchart TD A[Social Media Stream] --> B[Data Collector] B --> C[Text Preprocessor] C --> D[Sentiment Analyzer] D --> E[Results Storage] D --> F[Real-time API] subgraph "Processing Pipeline" C D end subgraph "Data Layer" E[(Database)] end subgraph "API Layer" F end ``` 2. Implementation Roadmap Let's build a Python-based solution using popular libraries. Here's our initial implementation: ```python # Required libraries import pandas as pd from textblob import TextBlob # Simple sentiment analysis for beginners import tweepy # For Twitter streaming (example social platform) from datetime import datetime ``` Data Preprocessing Steps: - Text cleaning (remove special characters, links) - Tokenization - Convert to lowercase - Remove stop words Let's implement a basic preprocessing pipeline: ```python import re import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords def preprocess_text(text): # Convert to lowercase text = text.lower() # Remove URLs text = re.sub(r'http\S+|www\S+|https\S+', '', text) # Remove special characters text = re.sub(r'[^\w\s]', '', text) # Tokenization tokens = word_tokenize(text) # Remove stopwords stop_words = set(stopwords.words('english')) tokens = [token for token in tokens if token not in stop_words] return ' '.join(tokens) ``` 3. Technical Deep-Dive Let's create a basic sentiment analyzer class: ```python import logging from textblob import TextBlob from datetime import datetime from collections import deque import threading import queue class RealTimeSentimentAnalyzer: def __init__(self, buffer_size=1000): self.buffer = deque(maxlen=buffer_size) self.processing_queue = queue.Queue() self.running = False self.setup_logging() def setup_logging(self): logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) self.logger = logging.getLogger(__name__) def analyze_sentiment(self, text): """ Analyze sentiment of given text using TextBlob. Returns: (polarity, subjectivity) Polarity: -1 to 1 (negative to positive) Subjectivity: 0 to 1 (objective to subjective) """ try: processed_text = preprocess_text(text) analysis = TextBlob(processed_text) return { 'text': text, 'processed_text': processed_text, 'polarity': analysis.sentiment.polarity, 'subjectivity': analysis.sentiment.subjectivity, 'sentiment': self.get_sentiment_label(analysis.sentiment.polarity), 'timestamp': datetime.now().isoformat() } except Exception as e: self.logger.error(f"Error analyzing sentiment: {str(e)}") return None def get_sentiment_label(self, polarity): """Convert polarity score to human-readable label""" if polarity > 0.3: return 'positive' elif polarity < -0.3: return 'negative' else: return 'neutral' def start_processing(self): """Start the background processing thread""" self.running = True self.processing_thread = threading.Thread(target=self._process_queue) self.processing_thread.start() def stop_processing(self): """Stop the background processing thread""" self.running = False self.processing_thread.join() def _process_queue(self): """Background thread to process texts in queue""" while self.running: try: text = self.processing_queue.get(timeout=1) result = self.analyze_sentiment(text) if result: self.buffer.append(result) self.logger.info(f"Processed text with sentiment: {result['sentiment']}") self.processing_queue.task_done() except queue.Empty: continue except Exception as e: self.logger.error(f"Error in processing thread: {str(e)}") def add_text(self, text): """Add text to processing queue""" self.processing_queue.put(text) def get_recent_results(self, limit=100): """Get most recent sentiment analysis results""" return list(self.buffer)[-limit:] def get_sentiment_summary(self): """Get summary statistics of recent sentiments""" results = self.get_recent_results() if not results: return {} sentiments = [r['sentiment'] for r in results] return { 'positive': sentiments.count('positive') / len(sentiments), 'neutral': sentiments.count('neutral') / len(sentiments), 'negative': sentiments.count('negative') / len(sentiments), 'total_processed': len(sentiments) } ``` 4. Quality Assurance Testing Strategy: ```python import unittest class TestSentimentAnalyzer(unittest.TestCase): def setUp(self): self.analyzer = RealTimeSentimentAnalyzer() def test_positive_sentiment(self): result = self.analyzer.analyze_sentiment("I love this product!") self.assertGreater(result['polarity'], 0) self.assertEqual(result['sentiment'], 'positive') def test_negative_sentiment(self): result = self.analyzer.analyze_sentiment("This is terrible!") self.assertLess(result['polarity'], 0) self.assertEqual(result['sentiment'], 'negative') ``` Evaluation Metrics: - Accuracy - Processing speed (texts/second) - Queue latency - System resource usage 5. Production Considerations Scaling Strategies: 1. Horizontal scaling: - Deploy multiple analyzer instances - Use load balancer - Implement message queue (Redis/RabbitMQ) 2. Monitoring Setup: ```python import psutil import time def monitor_performance(analyzer): while True: stats = analyzer.get_sentiment_summary() print(f"Processed: {stats['total_processed']}") print(f"CPU Usage: {psutil.cpu_percent()}%") print(f"Memory Usage: {psutil.virtual_memory().percent}%") time.sleep(60) ``` Usage Example: ```python # Initialize and start analyzer analyzer = RealTimeSentimentAnalyzer() analyzer.start_processing() # Process some text analyzer.add_text("I'm really enjoying this new feature!") # Get results results = analyzer.get_recent_results() summary = analyzer.get_sentiment_summary() # Clean shutdown analyzer.stop_processing() ``` Common Issues & Solutions: 1. High CPU Usage - Increase buffer size - Implement batch processing - Add rate limiting 2. Memory Leaks - Monitor buffer size - Implement periodic cleanup - Use memory profiling 3. Slow Processing - Optimize preprocessing - Implement caching - Consider batch processing Would you like me to elaborate on any particular aspect or provide more specific examples for your use case?