High-quality NLP models start with high-quality labeled data. Learning Spiral provides accurate text annotation services, including entity recognition, sentiment tagging, intent classification, and text categorization. Our skilled annotators ensure clean, consistent, and context-aware labeling, helping businesses build reliable AI models for chatbots, search engines, content moderation, and language understanding applications.
Text Annotation
Text Classification
Learning Spiral’s text classification services organize unstructured text into meaningful categories, enabling machines to understand content at scale. From topic tagging to spam detection and document sorting, our expert annotators deliver accurate, consistent labels that train NLP models for smarter search, content moderation, and automated decision-making across industries
Entity Tagging
Our entity tagging services identify and label key information within text — names, locations, dates, organizations, and more. This precise, structured annotation helps AI models extract meaningful insights from raw text, powering applications like information retrieval, chatbots, and document processing with higher accuracy and contextual understanding.
Intent Recognition
Learning Spiral’s intent recognition annotation captures the true purpose behind user queries and conversations. By accurately labeling intent within text data, we help train conversational AI, virtual assistants, and chatbots to understand user needs better, delivering faster, more relevant responses and improved customer experience across platforms.
Sentiment Analysis
Our sentiment analysis labeling services classify text by emotional tone — positive, negative, or neutral — helping businesses understand customer opinions and feedback. Trained on high-quality annotated datasets, AI models can accurately gauge public sentiment from reviews, social media, and support tickets, driving smarter, data-backed business decisions.