Abstract
The Vehicle Dependability Study (VDS) is a survey study on customer satisfaction for vehicles that have been sold for three years. VDS data analytics plays an important role in the vehicle development process because it can contribute to enhancing the brand image and sales of an automobile company by properly reflecting customer requirements retrieved from the analysis results when developing the vehicle's next model. Conventional approaches to analyzing the voice of customers (VOC) data, such as VDS, have focused on finding the mainstream of customer responses, many of which are already known to the enterprise. However, detecting and visualizing notable opinions from a large amount of VOC data are important in responding to customer complaints. In this study, we propose a framework for identifying unusual but significant customer responses and frequently used words therein based on distributed document representation, local outlier factor, and TF–IDF methods. We also propose a procedure that can provide useful information to vehicle engineers by visualizing the main results of the framework. This unusual customer response detection and visualization framework can accelerate the efficiency and effectiveness of many VOC data analytics.
Original language | English |
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Article number | 113111 |
Journal | Expert Systems With Applications |
Volume | 144 |
DOIs | |
Publication status | Published - 2020 Apr 15 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. NRF-2019R1F1A1060338 ) and Korea Electric Power Corporation (Grant number: R18XA05 ). Thanks to an anonymous reviewer for enabling quantitative validation of the proposed framework using fabrication data.
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. NRF-2019R1F1A1060338) and Korea Electric Power Corporation (Grant number: R18XA05). Thanks to an anonymous reviewer for enabling quantitative validation of the proposed framework using fabrication data.
Publisher Copyright:
© 2019
Keywords
- Keyword network
- Local outlier factor
- TF-IDF
- Voice of customers
ASJC Scopus subject areas
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence