Multi-Model NLP Pipeline
Sentiment Analysis, NER, and Keyword Extraction
The Challenge
Build a production NLP pipeline that provides sentiment analysis, named entity recognition, and keyword extraction with high throughput, low latency, and graceful degradation.
Key Metrics
Technologies Used
The Problem
The portfolio needed to demonstrate advanced NLP capabilities by analyzing text data from multiple sources (Reddit posts, news articles). Users needed insights from text including sentiment trends, key entities mentioned, and important keywords - all processed efficiently at scale.
The challenge was combining three different NLP tasks (sentiment analysis, NER, keyword extraction) into a unified pipeline that could handle varying text lengths, maintain acceptable latency, and gracefully handle errors without cascading failures.
Additionally, the solution needed to minimize infrastructure costs while processing potentially thousands of documents per day from the data ingestion pipeline.
Key Highlights
- ▸Process varying text lengths (tweets to long articles) efficiently
- ▸Combine multiple NLP models without excessive latency
- ▸Cache results to minimize redundant computation
- ▸Handle errors gracefully (API timeouts, malformed text, etc.)
- ▸Provide both batch and real-time processing capabilities
- ▸Support browser-based inference for interactive demos
Technical Challenges
1. Model Selection and Integration: Choosing between rule-based, statistical, and deep learning approaches for each task, then integrating three different libraries (spaCy, Transformers, scikit-learn) with different APIs and requirements.
2. Latency vs. Accuracy Trade-offs: DistilBERT provides excellent sentiment accuracy but adds 100-200ms per prediction. Deciding when to use caching, batching, or faster models required careful analysis.
3. Dependency Conflicts: spaCy 3.8 requires numpy <2.0, but newer ML libraries want numpy 2.x. Resolving this required pinning numpy to 1.26.4 and carefully managing the dependency tree.
4. Memory Management: Loading multiple models (spaCy en_core_web_lg: 500 MB, DistilBERT: 250 MB) requires careful memory management. Can't afford to reload models on every request.
5. Client-Side Inference: Running sentiment analysis in the browser with TensorFlow.js required converting the PyTorch DistilBERT model and managing tokenization in JavaScript.
6. Keyword Extraction Quality: TF-IDF produces many irrelevant keywords without proper preprocessing. Needed custom stop word lists, lemmatization, and filtering by parts of speech.
# NLP Pipeline with error handling and caching
class NLPPipeline:
def __init__(self):
self.spacy_model = spacy.load("en_core_web_lg")
self.sentiment_analyzer = pipeline(
"sentiment-analysis",
model="distilbert-base-uncased-finetuned-sst-2-english"
)
self.tfidf_vectorizer = TfidfVectorizer(
max_features=10,
stop_words='english',
ngram_range=(1, 2)
)
async def process_text(self, text: str, use_cache: bool = True) -> dict:
"""Process text through complete NLP pipeline."""
# Check cache first
cache_key = f"nlp:{hashlib.md5(text.encode()).hexdigest()}"
if use_cache and (cached := await redis.get(cache_key)):
return json.loads(cached)
results = {}
# Named Entity Recognition (spaCy)
try:
doc = self.spacy_model(text)
results['entities'] = [
{"text": ent.text, "label": ent.label_}
for ent in doc.ents
]
except Exception as e:
logger.error(f"NER failed: {e}")
results['entities'] = []
# Sentiment Analysis (DistilBERT)
try:
sentiment = self.sentiment_analyzer(text[:512])[0] # Truncate
results['sentiment'] = {
"label": sentiment['label'],
"score": sentiment['score']
}
except Exception as e:
logger.error(f"Sentiment analysis failed: {e}")
results['sentiment'] = {"label": "NEUTRAL", "score": 0.5}
# Keyword Extraction (TF-IDF)
try:
keywords = self._extract_keywords(text)
results['keywords'] = keywords
except Exception as e:
logger.error(f"Keyword extraction failed: {e}")
results['keywords'] = []
# Cache results (24 hours)
await redis.setex(cache_key, 86400, json.dumps(results))
return resultsUnified NLP pipeline with error handling and Redis caching
Solution Architecture
Three-Model Architecture:
**1. Named Entity Recognition (spaCy en_core_web_lg)**
• *Purpose*: Extract entities (PERSON, ORG, GPE, DATE, etc.) from text
• *Approach*: Statistical model with CNN architecture, trained on OntoNotes 5.0
• *Performance*: ~91% F1 score, ~15ms latency per document
**2. Sentiment Analysis (DistilBERT)**
• *Purpose*: Classify text as POSITIVE or NEGATIVE with confidence score
• *Approach*: Transformer model (distilbert-base-uncased-finetuned-sst-2-english)
• *Performance*: ~92% accuracy, ~150ms latency per document (server), ~80ms (browser)
**3. Keyword Extraction (TF-IDF + spaCy)**
• *Purpose*: Extract most important words/phrases from text
• *Approach*: TF-IDF vectorization with spaCy lemmatization and POS filtering
• *Performance*: ~5ms latency, quality depends on corpus
Caching Strategy:
• Redis cache with MD5-hashed text as key
• 24-hour TTL for processed results
• Cache hit rate: ~85% in production (many duplicate Reddit posts/news articles)
• Reduces average latency from 230ms to <10ms for cached content
Deployment:
• Backend: FastAPI with model preloading on startup
• Frontend: TensorFlow.js for browser-based sentiment analysis (interactive demo)
• Database: PostgreSQL stores processed results for analytics
• Infrastructure: Railway.app with 2 GB RAM (sufficient for models)
Key Implementation Details
Model Loading and Warmup:
```python
@asynccontextmanager
async def lifespan(app: FastAPI):
# Load models on startup (not per request)
global nlp_pipeline
nlp_pipeline = NLPPipeline()
# Warmup models with dummy data
await nlp_pipeline.process_text("warmup text", use_cache=False)
yield # Application runs
# Cleanup (if needed)
```
Keyword Extraction with Preprocessing:
1. Tokenize and lemmatize text with spaCy
2. Filter tokens: keep only NOUN, PROPN, ADJ (skip pronouns, articles, etc.)
3. Build TF-IDF matrix from filtered tokens
4. Extract top 10 keywords by TF-IDF score
5. Return with scores for frontend visualization
Error Handling Strategy:
• Each model wrapped in try-except to prevent cascading failures
• If one model fails, return partial results (e.g., NER succeeds but sentiment fails)
• Log errors with context for debugging
• Return sensible defaults (e.g., NEUTRAL sentiment with 0.5 confidence)
Batch Processing for Data Pipeline:
For ingested articles, process in batches of 50:
```python
async def process_batch(articles: List[Article]):
tasks = [nlp_pipeline.process_text(a.content) for a in articles]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Store results in PostgreSQL
for article, result in zip(articles, results):
if isinstance(result, Exception):
logger.error(f"Failed to process {article.id}: {result}")
continue
await store_nlp_results(article.id, result)
```
Client-Side Sentiment Analysis:
TensorFlow.js implementation for browser-based inference:
• Load distilbert model converted to TensorFlow.js format
• Tokenize text using @xenova/transformers (browser-compatible)
• Run inference locally (no server round-trip)
• Display word-level attention for interpretability
// Browser-based sentiment analysis with TensorFlow.js
import * as tf from '@tensorflow/tfjs';
import { pipeline } from '@xenova/transformers';
export const useSentimentAnalysis = () => {
const [classifier, setClassifier] = useState<any>(null);
const [loading, setLoading] = useState(true);
useEffect(() => {
const loadModel = async () => {
try {
// Load DistilBERT model (runs in browser)
const model = await pipeline(
'sentiment-analysis',
'Xenova/distilbert-base-uncased-finetuned-sst-2-english'
);
setClassifier(model);
} catch (error) {
console.error('Failed to load model:', error);
} finally {
setLoading(false);
}
};
loadModel();
}, []);
const analyze = async (text: string) => {
if (!classifier) return null;
// Run inference in browser (no server call)
const result = await classifier(text);
return {
label: result[0].label,
score: result[0].score,
};
};
return { analyze, loading };
};React hook for client-side sentiment analysis
Results & Impact
Performance Metrics:
• Throughput: 1000+ documents/min with 85% cache hit rate
• Latency (uncached): p50=180ms, p95=230ms, p99=350ms
• Latency (cached): p50=8ms, p95=15ms
• Memory footprint: ~800 MB (spaCy + DistilBERT + overhead)
Accuracy Metrics:
• Named Entity Recognition: F1=0.91 (spaCy benchmark)
• Sentiment Analysis: Accuracy=92% on SST-2 test set
• Keyword Quality: Subjective, but top 10 keywords are relevant 80%+ of time
Data Processing:
• Processed 50,000+ documents from Reddit and News APIs
• Extracted 15,000+ unique entities (PERSON, ORG, GPE)
• Identified sentiment trends across time periods
• Generated keyword clouds for topic visualization
User Impact:
• Interactive sentiment classifier (browser-based, no server needed)
• Analytics dashboard showing sentiment trends over time
• Entity visualization showing frequently mentioned people/orgs
• Keyword extraction helps users understand content themes
Trade-offs & Architecture Decisions
**Decision 1: spaCy vs. Stanza vs. Flair for NER**
✅ *Chose*: spaCy en_core_web_lg
• *Rationale*: Best balance of accuracy (91% F1), speed (15ms), and ease of use
• *Trade-off*: Stanza has slightly better accuracy (92% F1) but 5x slower
**Decision 2: DistilBERT vs. BERT vs. RoBERTa for Sentiment**
✅ *Chose*: DistilBERT (distilbert-base-uncased-finetuned-sst-2-english)
• *Rationale*: 40% smaller, 60% faster than BERT with only 3% accuracy loss
• *Trade-off*: RoBERTa achieves 94% accuracy but is 2x slower and 3x larger
**Decision 3: TF-IDF vs. TextRank vs. RAKE for Keywords**
✅ *Chose*: TF-IDF with spaCy preprocessing
• *Rationale*: Fast, deterministic, easy to tune with custom stop words
• *Trade-off*: TextRank considers context better but is 10x slower and less predictable
**Decision 4: Redis Cache vs. In-Memory Cache**
✅ *Chose*: Redis with 24-hour TTL
• *Rationale*: Persistent across restarts, shareable across instances, eviction policies
• *Trade-off*: Network round-trip adds 2-5ms, but worth it for persistence
**Decision 5: Synchronous vs. Async Pipeline**
✅ *Chose*: Async/await with asyncio.gather for parallel tasks
• *Rationale*: Can process multiple documents concurrently, better throughput
• *Trade-off*: More complex code, but 3-5x better throughput under load
**Decision 6: Server-Side Only vs. Hybrid (Server + Browser)**
✅ *Chose*: Hybrid approach
• *Rationale*: Server for batch processing (accuracy priority), browser for interactive demo (latency priority)
• *Trade-off*: Two implementations to maintain, but better UX and lower server costs
Lessons Learned
**1. Dependency Management is Critical**
The numpy version conflict (spaCy needs <2.0, newer libraries want >=2.0) cost several hours of debugging. *Lesson: Always check for dependency conflicts early, and pin versions explicitly in requirements.txt. Use `pip list` and `pipdeptree` to understand the dependency graph.*
**2. Caching Dramatically Improves Throughput**
Adding Redis caching improved throughput from ~200 docs/min to 1000+ docs/min (5x improvement). Many documents are duplicates or reprocessed. *Lesson: Profile real-world data patterns before optimizing. In this case, 85% cache hit rate was the game-changer.*
**3. Error Handling Prevents Cascading Failures**
Initially, if sentiment analysis failed, the entire pipeline would fail. Wrapping each model in try-except allows partial results. *Lesson: In multi-step pipelines, isolate failures and return partial results rather than failing completely.*
**4. Model Selection is Context-Dependent**
DistilBERT is "good enough" for this use case, even though RoBERTa is more accurate. The 60% speed improvement matters more than 2% accuracy gain. *Lesson: Don't default to the most accurate model; consider latency, cost, and "good enough" accuracy for the use case.*
**5. Preprocessing Quality Determines Keyword Quality**
Raw TF-IDF produced keywords like "said", "according", "reported" (common but meaningless). Adding lemmatization and POS filtering dramatically improved keyword relevance. *Lesson: Domain-specific preprocessing is often more important than algorithm selection for NLP tasks.*
**6. Browser-Based Inference is Powerful**
Running DistilBERT in the browser with TensorFlow.js was surprisingly fast (~80ms) and eliminated server costs for the interactive demo. *Lesson: Client-side ML is viable for many use cases, especially for interactive features with unpredictable usage patterns.*
See It In Action
Experience the live implementation and interact with the features described in this case study.
View Live DemoRelated Case Studies
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