Text mining and text analytics – Analysis of data from a text that aims to obtain quality and structured information, as well as creating new meanings and information. Sources for the analysis can be text from everyday speech and data applications (email, RSS, CAD, database, etc.).
Concept and entity extraction – Obtaining information from structured or unstructured text, which meet some predefined criteria. The data obtained are a variety of topics or special entities such as names, places, companies, organizations, etc.. which are classified for further use.
Concept analysis – Obtained by processing the concept in order to seek relationships with other concepts. Links with other concepts are defined by terms, by the concept of context and content.
Natural language processing (NLP) – Automated application of the results of concept analysis to determine the meaning of human articulated assertions or queries using computational linguistics.
Content data normalizing – Converting barely structured content into a structured, formalized and standardized format of the content.
Federating and de-duplicating – The procedure that applies to any content that is in our field of interest, which is indexed with the aim of every item is currently in the same format, which completely eliminates the same results. This procedure allows an organization similar results in an easily understandable form for easy analysis and assessment.
Opinion mining(Sentiment analysis) – It is the process of determining attitudes and opinions of the speaker or writer's analysis of the text using the natural language processing, computational linguistics and text in relation to a theme and / or concept.
Auto-categorization – Involves the application of conceptual analysis, ontology and vocabulary, with the aim of organizing content by category, topic and entity.
Posted by: Dejan Petrovic