The data science for textual information; it refers to the ability to automatically recognize, process, analyze and predict the underlying meaning, message and/or sentiment in unstructured textual data. Typical questions to be answered: 

  • Is your business document/text driven?
  • Is document processing a key element of your daily operations?
  • Can the content be interpreted?
  • How can predictive value of textual information be unlocked?

Data cases: 

  • Document categorisation, tagging, enrichment and summarization
  • Sentiment analysis-based workflows
  • Data quality detection in text
  • Automated message triage and dispatching systems

Data story

"A software developer for legal services required a more automated process for document content handling. Cropland introduced a big data text mining methodology based on specific terminology and key words that could be found in the documents. Instead of the full ‘start-from-scratch’ manual labeling activities in the past, Cropland was able to develop an algorithm, which screened contents and auto-proposed labeling only to be checked and/or corrected by the companies’ editors."