ACIS Introduction to Data Analytics & Business Intelligence Text Mining Data Cleaning

Concept Map Text Mining Implementation Mixed Cell References Design: Accuracy Random Search, Left, Right, Mid, Len, & Paste Values

Objectives Define Text Mining Demonstrate Excel features that support text mining.

Segment A: Text Mining

Text Analytics / Text Mining Software that searches vast amounts of textual data (unstructured) identifying patterns.

Nestle Nestle processes Social Media 026?videoId=

Segment B: Text Functions

Text Mining Search Parse Concatenate SEARCH LEFT, MID, RIGHT, LEN &

Name Example Open Grades Textfile.xlsx. Divide Last Name, First Name into two separate columns. 1.Locate the comma (SEARCH) 2.Extract all characters to left of comma (LEFT) 3.Locate end of full name (LEN) 4.Extract almost all characters between comma and end of name (RIGHT)

SEARCH Function

LEFT Function

LEN or Length Function

RIGHT Function

MID Function Extract the first initial of first name.

Concatenate Combine First Name, space and Last Name. & is the concatenate symbol Quotes are required around constant strings of text

Student ID Example Extract each student’s PID from their address. Create a new student identifier by combining the first three letters of the last name with the last four digits of the student ID number.

Segment C: Data Cleaning & Generation

Data Cleaning Delete Unnecessary Columns & Rows Resize Columns Format Numeric Values Separate Distinct Values Shorten Lengthy Values Data Validation for Future Entries Generate Values

Favorite Pie Example

1.Ensure pie flavor data is consistent. 2.Replace confidential clicker ID # with randomly generated 6 digit number. 3.Ensure new ID number is static and unique.

Favorite Pie Example OriginalSortedConsistent

Random Number Functions =RAND() =RANDBETWEEN(low#, high#)

Paste Special - Values MAC: Edit Menu, Paste Special

Exam Feedback Example Open Exam Feedback.xlsx