NLP Revolution

Natural Language Processing enables machines to understand, interpret, and generate human language. Modern NLP achieves 90-95% accuracy on sentiment analysis, 85-92% on NER, and human-level performance on many tasks.

Core NLP Tasks

1. Sentiment Analysis

• Binary Classification: Positive/negative sentiment (92-96% accuracy)
• Multi-class: Very negative, negative, neutral, positive, very positive
• Aspect-based: Sentiment per product feature
• Emotion Detection: Joy, anger, sadness, fear, surprise

2. Named Entity Recognition (NER)

• Extract persons, organizations, locations, dates, monetary values
• Custom entities: product names, medical terms, legal references
• 85-92% F1 score on standard datasets
• Multi-language support (50+ languages)

3. Text Classification

• Topic classification, intent detection, spam filtering
• Multi-label classification (document can have multiple categories)
• Zero-shot classification with LLMs
• 90-96% accuracy on domain-specific tasks

4. Information Extraction

• Relation extraction between entities
• Event extraction from news articles
• Keyphrase extraction
• Document summarization

Technology Stack

Models: BERT, RoBERTa, DistilBERT, ELECTRA, DeBERTa

Frameworks: Hugging Face Transformers, spaCy, NLTK, AllenNLP

Deployment: FastAPI, TorchServe, TensorFlow Serving

Use Cases

• Customer Feedback: Analyze reviews, surveys, support tickets
• Social Media Monitoring: Track brand sentiment, trending topics
• Document Processing: Extract key information from contracts, invoices
• Content Moderation: Detect hate speech, toxic content
• Search & Discovery: Semantic search, query understanding

Pricing

• Sentiment Analysis: ₹8-20L
• NER System: ₹12-30L
• Custom NLP Pipeline: ₹25-60L
• Timeline: 6-12 weeks

Case Study: E-commerce Review Analysis

• Data: 500K product reviews
• Solution: Aspect-based sentiment analysis with BERT
• Results: 94% accuracy, identified 12 key pain points, led to ₹3.2Cr product improvements