Real-Time Sentiment Analysis ML Pipeline

Project Overview

Built an end-to-end ML pipeline for real-time sentiment analysis at scale, processing 50M+ social media posts daily across multiple platforms to provide brand monitoring and market insights.

Business Problem

A market intelligence company needed to:

• Monitor brand sentiment across Twitter, Reddit, News
• Detect emerging trends and crises in real-time
• Provide sentiment analytics to enterprise clients
• Support 100+ brands with sub-minute latency

Challenges:

• Volume: 50M+ posts/day (600+ QPS)
• Velocity: Real-time processing required
• Variety: Multiple platforms, languages, formats
• Accuracy: 85%+ sentiment classification required
• Scale: Cost-effective infrastructure

Architecture

System Design

┌──────────┐     ┌─────────┐     ┌──────────┐     ┌──────────┐
│ Data     │────▶│ Kafka   │────▶│ Spark    │────▶│ Storage  │
│ Ingestion│     │ Streams │     │ Processing│    │ & API    │
└──────────┘     └─────────┘     └──────────┘     └──────────┘
                       │                │              │
                       │                │              │
                  ┌────▼────┐      ┌───▼────┐    ┌───▼────┐
                  │ Schema  │      │  ML    │    │ Time   │
                  │Registry │      │ Models │    │ Series │
                  └─────────┘      └────────┘    └────────┘

Technology Stack

• Streaming: Apache Kafka, Kafka Streams
• Processing: Apache Spark (PySpark), Pandas
• ML: TensorFlow, Hugging Face Transformers, Scikit-learn
• Orchestration: Apache Airflow
• Storage: PostgreSQL, TimescaleDB, Redis
• Infrastructure: Docker, Kubernetes, AWS
• Monitoring: Prometheus, Grafana, MLflow

Implementation

1. Data Ingestion Layer

Multi-Source Connectors:

from kafka import KafkaProducer
import json
from typing import Dict

class SocialMediaConnector:
    def __init__(self, kafka_bootstrap_servers: str):
        self.producer = KafkaProducer(
            bootstrap_servers=kafka_bootstrap_servers,
            value_serializer=lambda v: json.dumps(v).encode('utf-8'),
            compression_type='gzip'
        )
    
    def ingest_twitter_stream(self):
        """Ingest Twitter real-time stream"""
        import tweepy
        
        class TwitterListener(tweepy.StreamListener):
            def on_status(self, status):
                post = self.transform_tweet(status)
                self.producer.send('social-media-raw', post)
        
        stream = tweepy.Stream(
            auth=twitter_auth,
            listener=TwitterListener()
        )
        stream.filter(track=self.tracked_keywords)
    
    def transform_tweet(self, status) -> Dict:
        """Normalize tweet format"""
        return {
            "id": status.id_str,
            "platform": "twitter",
            "text": status.full_text,
            "author": status.user.screen_name,
            "created_at": status.created_at.isoformat(),
            "metrics": {
                "likes": status.favorite_count,
                "retweets": status.retweet_count,
                "followers": status.user.followers_count
            },
            "metadata": {
                "language": status.lang,
                "location": status.user.location
            }
        }

2. Stream Processing

Kafka Streams for Real-Time Processing:

from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col, udf
from pyspark.sql.types import StructType, StringType, FloatType

class StreamProcessor:
    def __init__(self):
        self.spark = SparkSession.builder \
            .appName("SentimentAnalysis") \
            .config("spark.jars.packages", 
                   "org.apache.spark:spark-sql-kafka-0-10_2.12:3.5.0") \
            .getOrCreate()
        
        self.model = self.load_sentiment_model()
    
    def process_stream(self):
        """Process Kafka stream with Spark"""
        # Read from Kafka
        stream_df = self.spark \
            .readStream \
            .format("kafka") \
            .option("kafka.bootstrap.servers", "localhost:9092") \
            .option("subscribe", "social-media-raw") \
            .option("startingOffsets", "latest") \
            .load()
        
        # Parse JSON
        schema = StructType() \
            .add("id", StringType()) \
            .add("platform", StringType()) \
            .add("text", StringType()) \
            .add("author", StringType()) \
            .add("created_at", StringType())
        
        parsed_df = stream_df \
            .select(from_json(col("value").cast("string"), schema).alias("data")) \
            .select("data.*")
        
        # Apply sentiment analysis
        sentiment_udf = udf(self.predict_sentiment, FloatType())
        
        result_df = parsed_df \
            .withColumn("sentiment_score", sentiment_udf(col("text"))) \
            .withColumn("sentiment_label", self.classify_sentiment(col("sentiment_score")))
        
        # Write to sink
        query = result_df \
            .writeStream \
            .format("kafka") \
            .option("kafka.bootstrap.servers", "localhost:9092") \
            .option("topic", "sentiment-results") \
            .option("checkpointLocation", "/checkpoints/sentiment") \
            .start()
        
        query.awaitTermination()
    
    def predict_sentiment(self, text: str) -> float:
        """Predict sentiment score (-1 to 1)"""
        prediction = self.model.predict(text)
        return float(prediction)

3. ML Model Development

Custom Sentiment Transformer:

import tensorflow as tf
from transformers import TFAutoModel, AutoTokenizer
import numpy as np

class SentimentTransformer:
    def __init__(self, base_model="distilbert-base-uncased"):
        self.tokenizer = AutoTokenizer.from_pretrained(base_model)
        self.model = self.build_model(base_model)
    
    def build_model(self, base_model):
        """Build custom sentiment model"""
        # Load pre-trained transformer
        transformer = TFAutoModel.from_pretrained(base_model)
        
        # Input layers
        input_ids = tf.keras.Input(
            shape=(128,), 
            dtype=tf.int32, 
            name="input_ids"
        )
        attention_mask = tf.keras.Input(
            shape=(128,), 
            dtype=tf.int32, 
            name="attention_mask"
        )
        
        # Transformer outputs
        outputs = transformer(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        
        # Classification head
        x = outputs.last_hidden_state[:, 0, :]  # CLS token
        x = tf.keras.layers.Dropout(0.3)(x)
        x = tf.keras.layers.Dense(256, activation='relu')(x)
        x = tf.keras.layers.Dropout(0.2)(x)
        
        # Multi-task outputs
        sentiment_score = tf.keras.layers.Dense(
            1, activation='tanh', name='sentiment_score'
        )(x)
        
        sentiment_class = tf.keras.layers.Dense(
            3, activation='softmax', name='sentiment_class'
        )(x)  # positive, neutral, negative
        
        model = tf.keras.Model(
            inputs=[input_ids, attention_mask],
            outputs=[sentiment_score, sentiment_class]
        )
        
        return model
    
    def train(self, train_data, val_data, epochs=5):
        """Train sentiment model"""
        self.model.compile(
            optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),
            loss={
                'sentiment_score': 'mse',
                'sentiment_class': 'categorical_crossentropy'
            },
            metrics={
                'sentiment_score': 'mae',
                'sentiment_class': 'accuracy'
            }
        )
        
        # Callbacks
        callbacks = [
            tf.keras.callbacks.EarlyStopping(
                monitor='val_loss',
                patience=3,
                restore_best_weights=True
            ),
            tf.keras.callbacks.ModelCheckpoint(
                'best_model.h5',
                save_best_only=True
            ),
            tf.keras.callbacks.TensorBoard(log_dir='./logs')
        ]
        
        history = self.model.fit(
            train_data,
            validation_data=val_data,
            epochs=epochs,
            callbacks=callbacks
        )
        
        return history
    
    def predict(self, texts: list) -> np.ndarray:
        """Batch prediction"""
        encodings = self.tokenizer(
            texts,
            padding=True,
            truncation=True,
            max_length=128,
            return_tensors='tf'
        )
        
        scores, classes = self.model.predict({
            'input_ids': encodings['input_ids'],
            'attention_mask': encodings['attention_mask']
        })
        
        return scores, classes

4. Model Serving

TensorFlow Serving with Load Balancing:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import tensorflow as tf
import numpy as np
from typing import List

app = FastAPI()

# Load model
model = tf.saved_model.load('/models/sentiment-v1.2')
tokenizer = load_tokenizer()

class SentimentRequest(BaseModel):
    texts: List[str]
    batch_size: int = 32

class SentimentResponse(BaseModel):
    scores: List[float]
    labels: List[str]
    confidence: List[float]

@app.post("/predict", response_model=SentimentResponse)
async def predict_sentiment(request: SentimentRequest):
    """Batch sentiment prediction"""
    try:
        # Tokenize
        inputs = tokenizer(
            request.texts,
            padding=True,
            truncation=True,
            return_tensors='tf'
        )
        
        # Predict
        scores, classes = model(inputs)
        
        # Process results
        labels = ['negative', 'neutral', 'positive']
        results = {
            "scores": scores.numpy().flatten().tolist(),
            "labels": [labels[np.argmax(c)] for c in classes.numpy()],
            "confidence": [float(np.max(c)) for c in classes.numpy()]
        }
        
        return SentimentResponse(**results)
    
    except Exception as e:
        raise HTTPException(500, f"Prediction failed: {str(e)}")

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "model_version": "1.2"}

5. Workflow Orchestration

Airflow DAGs for Model Training:

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'ml-team',
    'depends_on_past': False,
    'email_on_failure': True,
    'email_on_retry': False,
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG(
    'sentiment_model_training',
    default_args=default_args,
    description='Retrain sentiment model weekly',
    schedule_interval='0 2 * * 0',  # Sunday 2AM
    start_date=datetime(2024, 1, 1),
    catchup=False
)

def extract_training_data():
    """Extract labeled data from last week"""
    db = connect_to_db()
    query = """
    SELECT text, sentiment_label, human_verified
    FROM social_posts
    WHERE created_at > NOW() - INTERVAL '7 days'
      AND human_verified = true
    """
    data = db.execute(query).fetchall()
    save_to_s3(data, 'training-data-latest.parquet')

def train_model():
    """Train new model version"""
    data = load_from_s3('training-data-latest.parquet')
    
    model = SentimentTransformer()
    history = model.train(data['train'], data['val'])
    
    # Save model
    model.save('models/sentiment-latest')
    
    # Log metrics to MLflow
    mlflow.log_metrics({
        'train_accuracy': history.history['accuracy'][-1],
        'val_accuracy': history.history['val_accuracy'][-1]
    })

def evaluate_model():
    """Evaluate on test set"""
    test_data = load_test_data()
    model = load_model('models/sentiment-latest')
    
    metrics = model.evaluate(test_data)
    
    # Compare with production model
    prod_model = load_model('models/production')
    prod_metrics = prod_model.evaluate(test_data)
    
    if metrics['accuracy'] > prod_metrics['accuracy']:
        promote_to_production()

# Define tasks
extract = PythonOperator(
    task_id='extract_data',
    python_callable=extract_training_data,
    dag=dag
)

train = PythonOperator(
    task_id='train_model',
    python_callable=train_model,
    dag=dag
)

evaluate = PythonOperator(
    task_id='evaluate_model',
    python_callable=evaluate_model,
    dag=dag
)

# Task dependencies
extract >> train >> evaluate

Results & Impact

Performance Metrics

Scale:

• Processing: 50M+ posts/day (600+ QPS sustained)
• Latency: <500ms end-to-end (p95)
• Throughput: 1000+ predictions/second per instance
• Availability: 99.95% uptime

ML Accuracy:

• Sentiment classification: 87.3% accuracy
• Multi-class F1 score: 0.85
• False positive rate: <3%
• Supports 12 languages

Cost Efficiency:

• Infrastructure cost: $0.002 per 1000 predictions
• 70% cost reduction vs. managed ML APIs
• Horizontal scaling: Linear cost with volume

Business Impact

• Client Satisfaction: 95% client retention rate
• Revenue: Enabled $5M ARR product line
• Insights: Detected 15 brand crises before viral spread
• Competitive Advantage: 10x faster than competitors

Technical Achievements

1. Real-Time at Scale: Sub-second latency at 600+ QPS
2. Custom Model: 5% better accuracy than off-the-shelf models
3. Multi-Language: Trained multilingual sentiment model
4. Production ML: Complete MLOps lifecycle with monitoring
5. Cost Optimized: 70% cheaper than managed solutions

Challenges & Solutions

Challenge 1: Streaming Backpressure

• Problem: Kafka backlog during traffic spikes
• Solution: Auto-scaling Spark consumers, partition tuning

Challenge 2: Model Drift

• Problem: Language evolves, slang changes
• Solution: Weekly retraining with recent data + active learning

Challenge 3: Multi-Language Support

• Problem: Need to support 12 languages
• Solution: Multilingual transformers (mBERT, XLM-RoBERTa)

Key Learnings

1. Streaming is Hard: Invest in monitoring and backpressure handling
2. Model Caching: Redis caching reduced duplicate predictions by 40%
3. Batch Processing: Batch inference 10x faster than individual requests
4. Feature Engineering: Domain-specific features improved accuracy 5%
5. Continuous Learning: Weekly retraining keeps model current

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Technologies: Python, TensorFlow, Kafka, Spark, Docker, Kubernetes, Airflow
Role: ML Engineer
Impact: $5M ARR enablement, 50M+ posts/day processing
Duration: 4 months development + ongoing