Multi-Language Natural Language Processing
Etuma Feedback Categorizer is a feedback analysis and reporting/API service, which applies various Rule-based Natural Language Processing techniques to analyze text-based customer feedback and use stuctured data to create filtering rules. The service contains multi-language morphological, syntactic, and semantic analysis components that together provide the user with information on the category of text and its sentiment. This information is collected, stored, classified, filtered and presented by industry specific categorization and and category specific sentiment.
Pasi Tapanainen, Ph.D.
Text is often ambiguous and dynamic. There are simply too many words and expressions and they evolve continuously. Hence keyword extraction doesn't work, as it uses fixed sets of pre-defined keyword lists. Etuma Feedback Categorizer understands language almost the same way as a human does. It recognizes verbs, nouns, adverbs etc. It uses artificial intelligence to pool keywords into themes (we call them industry specific categories). We have over a dozen different industry templates already in place. This turns open-text into statistical information easily and fast.