Template-type: ReDIF-Paper 1.0
Author-Name: Piyanuch Chaipornkaew
Author-Workplace-Name: College of Innovative Technology and Engineering Dhurakij Pundit UniversityBangkok, Thailand.
Author-Name: Takorn Prexawanprasut
Author-Workplace-Name:
 College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok, Thailand.
Author-Name:
 Chia-Lin Chang
Author-Workplace-Name:
 Department of Applied Economics Department of FinanceNational Chung Hsing University Taichung, Taiwan.
Author-Name:
 Michael McAleer
Author-Workplace-Name:
 Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of 
	Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of 
	Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.
Title: A Generalized Email Classification System for Workflow Analysis
Abstract: One of the most powerful internet communication channels is email. As employees and their clients communicate primarily via email, much 
	crucial business data is conveyed via email content. Where businesses are understandably concerned, they need a sophisticated workflow 
	management system to manage their transactions. A workflow management system should also be able to classify any incoming emails into 
	suitable categories. Previous research has implemented a system to categorize emails based on the words found in email messages. Two 
	parameters affected the accuracy of the program, namely the number of words in a database compared with sample emails, and an acceptable 
	percentage for classifying emails. As the volume of email has become larger and more sophisticated, this research classifies email 
	messages into a larger number of categories and changes a parameter that affects the accuracy of the program. The first parameter, namely 
	the number of words in a database compared with sample emails, remains unchanged, while the second parameter is changed from an 
	acceptable percentage to the number of matching words. The empirical results suggest that the number of words in a database compared with 
	sample emails is 11, and the number of matching words to categorize emails is 7. When these settings are applied to categorize 12,465 
	emails, the accuracy of this experiment is approximately 65.3%. The optimal number of words that yields high accuracy levels lies between 
	11 and 13, while the number of matching words lies between 6 and 8.
Classification-JEL: J24, O31, O32, O33.
Keywords: Email; business data; workflow management system; business transactions.
Length: 21 pages 
Creation-Date: 2017-07
Number: 2017-21
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1721.txt
File-URL: https://eprints.ucm.es/id/eprint/44630/1/1721.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1721