Services include text classification, sentiment analysis, named entity recognition, language detection, text summarization, question-answering system development, search and information retrieval, and custom NLP pipeline building.

Traditional NLP uses statistical and rule-based methods or smaller task-specific models for text processing. Large language models like GPT-4 or Claude use deep learning on massive datasets to understand and generate text with far greater flexibility. Modern NLP projects often combine both approaches.

Yes. Sentiment analysis, topic modeling, and category classification applied to customer feedback is one of the most common and high-value NLP use cases for businesses. A freelancer can build a system that processes thousands of reviews automatically to surface actionable insights.