Transforming content into business insight
Information-rich organizations need to extract meaning, nuance and context from vast amounts of unstructured content in order to harness its true business potential. OpenText™ Content Analytics helps you do just that for pretty much any business domain.
- Create machine-readable content from unstructured data
- Connect people with relevant content
- Discover valuable factual information to support decisions
- Boost productivity and reduce business risk
- Create machine-readable content from unstructured data: Content Analytics transforms your "messy" unstructured content into highly structured standards compliant formats that can be easily understood by computers, linked to other valuable data and delivered through any channel.
- Connect people with relevant content: Content Analytics enriches your content with semantic metadata to make it vastly more findable and easier to visually navigate. Let your content find the people that need it, rather than the other way around.
- Discover valuable factual information to support decisions: Content Analytics facilitates the identification of facts, trends, events and other relationships in large document collections allowing identification of business issues and understanding of their root causes in almost real-time.
- Boost productivity and reduce business risk: Content Analytics helps you determine what content to keep, what to throw away and massively reduces the time required to make these decisions. Once content is accurately classified it's easier to repurpose and it reduces exposure to legal risk and expensive eDiscovery requests.
By unlocking the value of content, whether residing in OpenText's content repositories or in any other information source, Content Analytics helps you deliver smarter solutions for customer experience management, brand and reputation monitoring, healthcare surveillance and early warning, eCommerce, competitive and market intelligence, eDiscovery and more.
OpenText Content Analytics is a multilingual advanced search and analytics platform with a powerful API that makes it effortless to embed in any business solution. The main features include:
- Concept Extraction: Concept Extraction is a linguistic and statistical analysis engine that is used to identify central concepts contained in any digital asset.
- Entity Extraction: Entity Extraction locates and extracts places, people, organizations, trademarks, products names, industry specific terminology and just about anything else you can think of.
- Categorization: Categorization automatically indexes and sorts content by category, according to an existing knowledge structure, whether representing an industry standard or a custom taxonomy. This is a powerful way to sort, visualize, and organize data.
- Sentiment Analysis: Sentiment Analysis detects the subjective nature of content and the entities contained within it, along with their tonality (positive, negative, neutral).
- Summarization: Summarization identifies key sentences in adigital asset and uses them to create a summary.
- Similarity Service: Similarity Service identifies similarity between digital assets based on their semantic profiles.
- Language Detection: Language Detection automatically identifies the language of a digital asset. It detects the following languages out-of-the-box: English, French, German, Arabic, Chinese (simplified), Czech, Danish, Estonian, Finnish, Hebrew, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Thai, Turkish, Ukrainian. Additional languages can be added via customization.
- Management Console: Content Analytics provides a Management Console that administrators and knowledge workers can use to modify and maintain controlled vocabulary in a flexible and simple manner (no programming expertise required).
Click the links below for more information about Content Analytics:
Semantic Navigation utilise la technologie OpenText Content Analytics pour indexer les pages web et le contenu des médias sociaux, pour une navigation intuitive utilisant des facettes sémantiques.
Sentiment Analysis Sentiment Analysis détecte la nature subjective du contenu et des entités contenues dans celui-ci, ainsi que leur tonalité (positive, négative, neutre).
Auto-Classification utilise la technologie OpenText Content Analytics pour classer automatiquement le contenu sur le serveur de contenu, en se basant sur les classifications existantes du Records management.