Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Analysis of IT Service Quality Elements Using Text Sentiment Analysis

텍스트 감정분석을 이용한 IT 서비스 품질요소 분석

  • Kim, Hong Sam (Department of Industrial & Management Engineering, Hannam University) ;
  • Kim, Chong Su (Department of Industrial & Management Engineering, Hannam University)
  • Received : 2020.09.20
  • Accepted : 2020.11.23
  • Published : 2020.12.31
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Abstract

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

Keywords

Acknowledgement

This work was supported by 2018 Hannam University Research Fund.

References

  1. Appel, O., Chiclana, F., and Carter, J., Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis, Journal of Acta Polytechnica Hungarica, 2015, Vol. 12, No. 3, pp. 87-108.
  2. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K., BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL-HLT, 2019.
  3. Jang, K., Park, S., and Kim, W.J., Automatic Construction of a Negative/Positive Corpus and Emotional Classification using the Internet Emotional Sign, Journal of KIISE, 2015, Vol. 42, No. 4, pp. 512-521. https://doi.org/10.5626/JOK.2015.42.4.512
  4. Kano, N., Seraku, N., Takahashi, F., and Tsuji, S., Attractive quality and must-be quality, The Journal of the Japanese Society for Quality Control, 1984, Vol. 14, No. 2, pp. 147-156.
  5. Kim, M., Jang, H., Jo, Y., and Shin, H., KOSAC(Korean Sentiment Analysis Corpus), Proceedings of Conference, Korean Institute of Information Scientists and Engineers, 2013, pp. 650-652.
  6. Kim, T., Analysis of Quality Characteristics of Smart Phone Using Modified Kano Model, Journal of Society of Korea Industrial and Systems Engineering, 2012, Vol. 35, No. 1, pp. 57-65.
  7. Landrum, H., Prybutok, V., Zhang, X., and Peak, D.A., Measuring IS System Service Quality with SERVQUAL : Users' Perceptions of Relative Importance of the Five SERVPERF Dimensions, Informing Science : The International Journal of an Emerging Transdiscipline, 2009, Vol. 12, pp. 17-35. https://doi.org/10.28945/426
  8. Lee, D., Lim, Y., and Kwon, T., Morpheme-based Efficient Korean Word Embedding, Journal of KIISE, 2018, Vol. 45, No. 5, pp. 444-450. https://doi.org/10.5626/jok.2018.45.5.444
  9. Lee, H. and Chung, Y., Development of Potential Customer Demand Improvement Index Based on Kano Model : Focused on Educational Service, Journal of Society of Korea Industrial and Systems Engineering, 2018, Vol. 41, No. 1, pp. 118-127. https://doi.org/10.11627/jkise.2018.41.1.118
  10. Liu, B., Sentiment Analysis : mining opinions, sentiments, and emotions, Cambridge University Press, 2015.
  11. Materla, T., Cudney, E., and Antony, J., The application of Kano model in the healthcare industry : a systematic literature review, Total Quality Management & Business Excellence, 2019, Vol. 30, No. 5-6, pp. 660-681. https://doi.org/10.1080/14783363.2017.1328980
  12. Pagano, D. and Maalej, W., User feedback in the app-store : An empirical study, 21st IEEE International Requirements Engineering Conference (RE), Rio de Janeiro, 2013, pp. 125-134.
  13. Parasuraman, A., Zeithaml, V.A., and Berry, L., SERV-QUAL: A multiple-item scale for measuring consumer perceptions of service quality, Journal of Retailing, 1998, Vol. 40, No. 1, pp. 12-37.
  14. Park, S., Na, C., Choi, M., Lee, D., and On, B., KNU Korean Sentiment Lexicon-Bi-LSTM-based Method for Building a Korean Sentiment Lexicon, Journal of Intelligence and Information Systems, 2018, Vol. 24, No. 4, 2018, pp. 219-240. https://doi.org/10.13088/JIIS.2018.24.4.219
  15. Park, Y. and Jung, H., Quality Improvement Priorities for Cosmetic Medical Service Using Kano Model and Potential Customer Satisfaction Improvement Index, Journal of Society of Korea Industrial and Systems Engineering, 2019, Vol. 42, No. 3, pp. 176-183. https://doi.org/10.11627/jkise.2019.42.3.176
  16. Suh, S. and Kim, J., Research Trend of Deep Learning-based Sentiment Analysis, Journal of Korea Multimedia Society, 2016, Vol. 20, No. 3, pp. 8-22.
  17. Timko, M., An experiment in continuous analysis, The Center for Quality of Management Journal, 1993, Vol. 2, No. 4, pp. 17-20.
  18. Walden, D., Kano's Methods for Understanding Customer-defined Quality, Center for Quality of Management Journal, 1993, Vol. 2, No. 4, pp. 6-20.
  19. Yoo, S. and Jeong, O., An Intelligent Chatbot Utilizing BERT Model and Knowledge Graph, Journal of Society for e-Business Studies, 2019, Vol. 24, No. 3, pp. 87-98.
  20. Zhang, J., Chen, D., and Lu, M., Combining Sentiment Analysis with a Fuzzy Kano Model for Product Aspect Preference Recommendation, IEEE Access, 2018, Vol. 6, pp. 59163-59172. https://doi.org/10.1109/access.2018.2875026
  21. Zhou, F., Ayoub, J., Xu, Q., and Yang, X.J., A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems, Journal of Mechanical Design, 2020, Vol. 142, No. 1, pp. 011101. https://doi.org/10.1115/1.4044435