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Do you need to describe or annotate your data? There are automated ways to generate text that describes your data set and the data that you might display in charts and graphs. Sometimes called Natural Language Processing or NLP, computer generated text takes the form of data descriptions, text analysis, sentiment, and text categorization.

Appian Analytics has significant experience in the key aspects of text analysis and automated text generation. What's more this capability can be embedded in your custom software application or provided in a database or report processing service request. Specific examples of text analysis include the following:

  • Graph Annotation - You can get automatically generated text that explains the data shown in charts and graphs.
  • Sentiment - Sentiment is the identification of whether statements are positive or negative. Using NLP technology, Appian Analytics can read large amounts of text comments and tag them for whether each comment is positive or negative.
  • Data Summarization - Generate high level text summaries of the data in a report page, report section, or the entire report. These intelligent summaries can both extract key measures to show and describe and also use advanced statistical techniques to illustrate relationships in the data.
  • Word Clouds - Word clouds are clear and concise ways to summarize large amounts of open end text. A word cloud can show the frequency of key word occurrence, key concepts in a text block, positive versus negative sentiment and more. Word clouds are often provided either as a simple graphic or as an interactive analysis where individual terms can be clicked and drilled to show the full comments containing the key word or phrase.
  • Uncover Findings - This is a form of "unsupervised learning" where a dataset is provided the NLP engine and the relationships in the data are automatically identified and explained to the end user.
  • Product or Service Reviews - Automatically scan a huge collection of product or service reviews and identify text analytics such as the sentiment of the comment (positive or negative), the category that the comments belongs, and even the probability that the comment is fake.

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