To use the Word Count tool, simply select the data then click on the QI Macros Menu and choose 'Word Count Wizard' from the Data & Text Mining sub menu: Here's a subset of the 35,000 healthcare denied claims comments: Most help desk software allows an export to Excel, so it was easy to get the text file. It may take awhile if there's lots of records, but it's much faster than trying to do it by hand or using Excel's COUNTIF statement. QI Macros Word Count tool will handle this quite easily. To do this, you will need software to parse the text into words and phrases. The goal is to summarize the various words and phrases to see if there's a Pareto pattern (80/20) to the text data. The first flavor of text mining attempts to look at the root cause of comments, a key method for Six Sigma.

Clustering of documents (e.g., affinity diagram).

Sentiment analysis to understand attitudes.

There are several types of text analysis: This variability makes text analysis more complex and challenging than plain numbers. In the healthcare denied claims example, reps spelled Medicare in various ways: MDCR, MedCR, Medicr, and so on.

So categorizing the text becomes difficult. Text data, by its very nature, is unstructured and varies from person to person.

Transcriptions of recorded voice such as doctor's notes.

What are some sources of text data for mining?

By some estimate, stored text contains as much as 80 percent of the key information needed for business improvement. Text mining, a subset of data mining, explores the written word to find hidden patterns. Text mining can also be useful for Six Sigma.Ĭompanies use data mining to explore large amounts of data to discover meaningful patterns and rules. You're already familiar with various forms of Text Mining: search engines, spam filters, spelling and grammar checking and language translation. This is where text mining comes to the rescue. Analyzing it by hand would have taken weeks. Every note was somewhat different depending on the rep who did the analysis. This text data, stored in a mainframe computer, was a rich source of information about the cause of denied claims, but extracting the data was considered too difficult because there were over 35,000 individual notes about individual cases.

Free Agile Lean Six Sigma Trainer Trainingįree Excel Tips » Text Mining in Excel Text Mining in Excel Count Words to Find Hidden Patterns in Text DataĪ healthcare company's service reps entered notes about why insurance claims were denied.

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Use quick analysis tool in excel to sum