{"id":405,"date":"2022-12-12T12:42:31","date_gmt":"2022-12-12T12:42:31","guid":{"rendered":"https:\/\/nasaff.com\/?page_id=405"},"modified":"2023-01-02T18:29:02","modified_gmt":"2023-01-02T18:29:02","slug":"gain-insights-from-textual-data-using-text-mining","status":"publish","type":"page","link":"https:\/\/nasaff.com\/en\/gain-insights-from-textual-data-using-text-mining","title":{"rendered":"Extracting insights from text data with text mining"},"content":{"rendered":"

Nahezu 80 % aller Unternehmensdaten passen in keine Datenbank<\/p>\n\n\n\n

Unstructured data contained in text documents, e-mails, Web pages, and various office documents does not reach the employees who need it to productively complete their tasks.<\/p>\n\n\n\n

Text mining uses machine learning to analyze these texts and determine the core information in an automated way. The process extracts essential information from texts available both on the web and in internal systems, and structures and semantically enriches them. In this way, you can leverage information from emails, Word- and PDF-documents, as well as numerous free texts available in social media, websites or tools.<\/p>\n\n\n\n

Use Cases<\/strong><\/h4>\n\n\n\n
Digitization and structuring of company knowledge<\/h6>\n\n\n\n
    \n
  • Automated creation of document management systems and databases<\/li>\n\n\n\n
  • Provision of searchable enterprise data with the help of knowledge data structures<\/li>\n<\/ul>\n\n\n\n
    Automated customer communication<\/h6>\n\n\n\n
      \n
    • Intelligent recognition and allocation of customer requests via e-mail<\/li>\n\n\n\n
    • Learned chatbots to handle frequent requests<\/li>\n<\/ul>\n\n\n\n
      Identification of the customer's interest<\/h6>\n\n\n\n
        \n
      • Identification of customer interests based on frequent search queries for targeted marketing activities<\/li>\n\n\n\n
      • Trend detection by monitoring social media<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"

        Nahezu 80 % aller Unternehmensdaten passen in keine Datenbank Unstrukturierte Daten, die in Textdokumenten, E-Mails, Webseiten und verschiedenen Office-Dokumenten enthalten sind, erreichen nicht die Mitarbeiter, die sie zur produktiven Erledigung ihrer Aufgaben ben\u00f6tigen. Text Mining nutzt maschinelles Lernen, um diese Texte zu analysieren und die Kerninformationen automatisiert zu ermitteln. Das Verfahren extrahiert wesentliche Informationen aus Texten, die sowohl im Web als auch in internen Systemen verf\u00fcgbar sind, und strukturiert und reichert sie semantisch an. Auf diese Weise nutzen Sie E-Mails, Word- und PDF-Dokumente sowie zahlreiche freie Texte, die in sozialen Medien, Websites oder Tools verf\u00fcgbar sind. Use Cases Digitalisierung und Strukturierung von Unternehmenswissen Automatisierte Kundenkommunikation Identifizierung des Kundeninteresses<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"class_list":["post-405","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/pages\/405","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/comments?post=405"}],"version-history":[{"count":5,"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/pages\/405\/revisions"}],"predecessor-version":[{"id":527,"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/pages\/405\/revisions\/527"}],"wp:attachment":[{"href":"https:\/\/nasaff.com\/en\/wp-json\/wp\/v2\/media?parent=405"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}