NDLI: Knowledge discovery of customer satisfaction and dissatisfaction using ontology-based text analysis of critical incident dialogues

Content Provider	IEEE Xplore Digital Library
Author	Trappey, C. Hsin-Ying Wu Kuan-Liang Liu
Copyright Year	2012
Description	Author affiliation: Department of Technology Management, Open University of Kaohsiung, Taiwan (Hsin-Ying Wu) \|\| Institute of Information Management, National Cheng Kung University, Tainan, Taiwan (Kuan-Liang Liu) \|\| Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan (Trappey, C.)
Abstract	Ontology based systems have long been recognized by researchers as the starting point for automated text analysis (or text mining) of consumer dialogues. Therefore, this research creates an ontology schema for consumer complaint dialogues related to mass rapid transportation systems. Based on the complaint ontology, the critical incident technique is used to construct an open-ended customer questionnaire to collect the positive and negative text dialogues of passengers describing their transportation experiences. Several valid and reliable methods have been developed to cluster significant text using the frequency of key words. An example would be the use of keyword frequency (KF) analysis and the formation of clusters based on KF to study patents and technology trends. The intention of this research is to use these methods to automatically text mine consumer dialogues, create significant dialogue clusters, and, from these clusters, derive meaningful trends, baselines, and interpretations of consumer satisfaction and dissatisfaction with a mass transit system in a major metropolitan city.
Starting Page	470
Ending Page	475
File Size	2073224
Page Count	6
File Format	PDF
ISBN	9781467312110
e-ISBN	9781467312127
DOI	10.1109/CSCWD.2012.6221860
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Color Colored noise Rails complaint cluster analysis customer complaint ontology critical incident techniques text mining keyword frequency analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Knowledge Discovery of Service Satisfaction Based on Text Analysis of Critical Incident Dialogues and Clustering Methods

A framework of customer complaint handling system

Semantic mining on customer survey

Using the Critical Incident Technique to Assess Gaming Customer Satisfaction

Using the Critical Incident Technique to Assess Gaming Customer Satisfaction

Cluster Analysis and Optimization in Color-Based Clustering for Image Abstract

Customer complaints.

Using the collective intelligence of sports fans to improve professional football league customer service

Meersman: Ontology-based Customer Complaint Management (2003)

Knowledge discovery of customer satisfaction and dissatisfaction using ontology-based text analysis of critical incident dialogues

Similar Documents

Knowledge Discovery of Service Satisfaction Based on Text Analysis of Critical Incident Dialogues and Clustering Methods

A framework of customer complaint handling system

Semantic mining on customer survey

Using the Critical Incident Technique to Assess Gaming Customer Satisfaction

Using the Critical Incident Technique to Assess Gaming Customer Satisfaction

Cluster Analysis and Optimization in Color-Based Clustering for Image Abstract

Customer complaints.

Using the collective intelligence of sports fans to improve professional football league customer service

Meersman: Ontology-based Customer Complaint Management (2003)

Knowledge discovery of customer satisfaction and dissatisfaction using ontology-based text analysis of critical incident dialogues