Conference paper Open Access

Visual Text Analytics for Technology and Innovation Management

Nazemi, Kawa; Burkhardt, Dirk

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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  "description": "<p>Through coupling of Data Mining, Visual Analytics and Business Analytics techniques, we created a novel solution for strategic market analysis with focus on early trend recognition. As fundament, we are able to consider a variety of text data, as for instance research publications available from a number of (open access) digital libraries, reports and other data from companies, web data about markets as well as news from companies or social media data etc. In an advanced and unified processing pipeline, the information is extracted and mined for a variety of analytical purposes. Via an interactive analysis user-interface, domain experts are able to analysis strong and weak signals in perspective of upcoming trends.</p>", 
  "license": "", 
  "creator": [
      "affiliation": "Darmstadt University of Applied Sciences", 
      "@id": "", 
      "@type": "Person", 
      "name": "Nazemi, Kawa"
      "affiliation": "Darmstadt University of Applied Sciences", 
      "@id": "", 
      "@type": "Person", 
      "name": "Burkhardt, Dirk"
  "headline": "Visual Text Analytics for Technology and Innovation Management", 
  "image": "", 
  "datePublished": "2019-09-13", 
  "url": "", 
  "version": "v1", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "Visual Text Analytics", 
    "Technology Management", 
    "Innovation Management", 
    "Text Analysis", 
    "Business Analytics", 
    "Trend Analytics"
  "@context": "", 
  "identifier": "", 
  "@id": "", 
  "workFeatured": {
    "url": "", 
    "alternateName": "ORM2019", 
    "location": "Darmstadt, Germany", 
    "@type": "Event", 
    "name": "OpenRheinMain Conference"
  "name": "Visual Text Analytics for Technology and Innovation Management"
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