DIGITAL LIBRARY ARCHIVE
HOME > DIGITAL LIBRARY ARCHIVE
< Previous   List   Next >  
An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels
Full-text Download
Hyun Sil Moon (School of Business & AI Management Research Center, KyungHee University)
David Sung (School of Business & AI Management Research Center, KyungHee University)
Jae Kyeong Kim (School of Business & AI Management Research Center, KyungHee University)
Vol. 25, No. 1, Page: 21 ~ 41
10.13088/jiis.2019.25.1.021
Keywords
service quality, topic mining, decision tree, big data analysis, online review analysis
Abstract
Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers’ online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.
Show/Hide Detailed Information in Korean
호텔 산업의 서비스 품질 향상을 위한 토픽 마이닝 기반 분석 방법
문현실 (경희대학교)
성다윗 (경희대학교)
김재경 (경희대학교)
Keywords
서비스 품질, 토픽 마이닝, 의사결정나무, 빅데이터 분석, 온라인 후기 분석
Abstract
정보 기술의 발전으로 온라인에서 활용 가능한 데이터의 양이 급속히 증대되고 있다. 이러한 빅데이터 시대에 많은 연구들이 통찰력을 발견하고 데이터의 효과를 입증하기 위해 노력하고 있다. 특히 관광 산업의 경우 정보에 민감한 사업으로 소셜 미디어의 영향력이 높고 소셜 미디어의 상품 후기에 소비자들이 영향을 많이 받아 많은 기업과 연구자들이 소셜 미디어를 분석하여 새로운 서비스 및 통찰력을 얻고자 시도하였다. 하지만 소셜 미디어의 후기는 텍스트로 이루어진 대표적인 비정형 데이터로적절한 처리를 하지 않으면 분석에 활용할 수 없다. 또한 후기 데이터의 양이 방대함에 따라 사람이직접 분석하기도 어려운 실정이다. 따라서, 본 연구에서는 이러한 소셜미디어 상의 온라인 후기로부터직접 호텔의 서비스 품질 향상을 위한 통찰력을 추출할 수 있는 분석 방법을 제시하고자 한다. 이를위해 본 연구에서는 먼저 후기 데이터에 포함되어 있는 주제어를 추출하는 토픽 마이닝 기법을 적용하였다. 토픽 마이닝은 대용량의 문서 집합으로부터 문서를 대표하는 단어 집합을 추출하는 기법을 의미하며 본 연구에서는 다양한 연구에서 활용되고 있는 LDA모형을 사용하여 토픽 마이닝을 수행하였다.
하지만, 토픽 마이닝 자체만으로는 주제어와 평점 사이의 관계를 도출할 수 없어 서비스 품질 향상을위한 통찰력을 발견하기 어렵다. 그에 따라 본 연구에서는 토픽 마이닝의 결과값을 기반으로 의사결정나무 모형을 사용하여 주제어와 평점 사이의 관계를 도출하였다. 이러한 방법론의 유용성을 평가하기위해 홍콩에 있는 4개 호텔의 온라인 후기를 수집하고 제안한 방법론의 분석 결과를 해석하는 실험을진행하였다. 실험 결과 긍정 후기를 통해 각 호텔이 유지해야할 서비스 영역을 발견할 수 있었으며부정 후기를 통해 개선해야할 서비스 영역을 도출할 수 있었다. 따라서, 본 연구에서 제안한 방법론을사용하여 방대한 양의 후기 데이터로부터 서비스 개선 및 유지 영역을 발견할 수 있으리라 기대된다.
Cite this article
JIIS Style
Moon, H. S., D. Sung, and J. K. Kim, "An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels", Journal of Intelligence and Information Systems, Vol. 25, No. 1 (2019), 21~41.

IEEE Style
Hyun Sil Moon, David Sung, and Jae Kyeong Kim, "An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels", Journal of Intelligence and Information Systems, vol. 25, no. 1, pp. 21~41, 2019.

ACM Style
Moon, H. S., Sung, D., and Kim, J. K., 2019. An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels. Journal of Intelligence and Information Systems. 25, 1, 21--41.
Export Formats : BiBTeX, EndNote

Warning: include(/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php) [function.include]: failed to open stream: No such file or directory in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429

Warning: include() [function.include]: Failed opening '/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php' for inclusion (include_path='.:/usr/local/php/lib/php') in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429
@article{Moon:JIIS:2019:759,
author = {Moon, Hyun Sil and Sung, David and Kim, Jae Kyeong},
title = {An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2019},
volume = {25},
number = {1},
month = Mar,
year = {2019},
issn = {2288-4866},
pages = {21--41},
url = {http://dx.doi.org/10.13088/jiis.2019.25.1.021 },
doi = {10.13088/jiis.2019.25.1.021},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { service quality, topic mining, decision tree, big data analysis and online review analysis
},
}
%0 Journal Article
%1 759
%A Hyun Sil Moon
%A David Sung
%A Jae Kyeong Kim
%T An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 25
%N 1
%P 21-41
%D 2019
%R 10.13088/jiis.2019.25.1.021
%I Korea Intelligent Information System Society