Exploratory text data analysis for quality hypothesis generation. Issue Volume 30:Issues 4(2018) (2nd October 2018)

• Record Type: Journal Article
• Authors: Allen, Theodore T.; Sui, Zhenhuan; Akbari, Kaveh
• Is Part Of: Quality engineering. Volume 30:Issues 4(2018)
• Page Start: 701
• Page End: 712
• Publication Date: 2018-10-02
• Subjects: cyber security -- exploratory data analysis -- graphical data analysis -- pattern discovery -- quality improvement -- text analytics -- twitter analysis; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2018.1481216
• Languages: English
• ISSNs: 0898-2112

Abstract

Abstract: Freestyle text data such as surveys, complaint transcripts, customer ratings, or maintenance squawks can provide critical information for quality engineering. Exploratory text data analysis (ETDA) is proposed here as a special case of exploratory data analysis (EDA) for quality improvement problems with freestyle text data. The EDTA method seeks to extract useful information from the text data to identify hypotheses for additional exploration relating to key inputs or outputs. The proposed four steps of ETDA are: (1) preprocessing of text data, (2) text data analysis and display, (3) salient feature identification, and (4) salient feature interpretation. Five examples illustrate the methods.