Open AccessAnalyzing unstructured data: text analytics in JMP
Author(s) -
Volker Kraft,
AUTHOR_ID
Publication year - 2017
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52041/srap.17204
Subject(s) - unstructured data , computer science , semantics (computer science) , information retrieval , data science , text mining , topic model , noisy text analytics , text messaging , feature (linguistics) , data mining , text graph , big data , world wide web , programming language , linguistics , philosophy
As much as 80% of all data is unstructured but still has exploitable information available. For example, unstructured text data could result from comment fields in surveys or incident reports. You want to explore this unstructured text to better understand the information that it contains. Text Mining, based on a transformation of free text into numerical summaries, can pave the way for new findings. This example of the new text mining feature in JMP starts with a multi-step text preparation using techniques like stemming and tokenizing. This data curation is pivotal for the subsequent analysis phase, exploring data clusters and semantics. Finally, combining text mining results with other structured data takes familiar multivariate analysis and predictive modeling to a next level.