• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.71-89
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• 2019

In order to come up with satisfying product and improvement, firms use traditional marketing research methods to obtain consumers' opinions and further try to reflect them. Recently, gathering data from consumer communication platforms like internet and SNS has become popular methods. Meanwhile, with the development of information technology, mobile companies are launching new digital products for children to protect them from harmful content and provide them with necessary functions and information. Among these digital products, Kids Phone, which is a wearable device with safe functions that enable parents to learn childern's location. Kids phone is relatively cheaper and simpler than smartphone but it is noted that there are several problems such as some useless functions and frequent breakdowns. This study analyzes the reviews of Kids phones from domestic mobile companies, identifies the characteristics, strengths and weaknesses of the products, proposes improvement methods strategies for devices and services through SNS consumer analysis. In order to do that customer review data from online shopping malls was gathered and was further analyzed through text mining methods such as TF/IDF, Sentiment Analysis, and network analysis. Customer review data was gathered through crawling Online shopping Mall and Naver Blog/$Caf\acute{e}$. Data analysis and visualization was done using 'R', 'Textom', and 'Python'. Such analysis allowed us to figure out main issues and recent trends regarding kids phones and to suggest possible service improvement strategies based on sentiment analysis.

• Information Systems Review
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• v.24 no.1
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• pp.157-181
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• 2022

As competition in the airline industry intensifies, effective airline service quality evaluation has become one of the main challenges. In particular, as big data analytics has been touted as a new research paradigm, new research on service quality measurement using online review analysis has been attempted. However, these studies do not use review titles for analysis, relyon supervised learning that requires a lot of human intervention in learning, and do not consider airline characteristics in classifying service quality dimensions.To overcome the limitations of existing studies, this study attempts to measure airlines service quality and to classify it into the AIRQUAL service quality dimension using online review text as well as title based on self-trainingand sentiment analysis. The results show the way of effective extracting service quality dimensions of AIRQUAL from online reviews, and find that each service quality dimension have a significant effect on service satisfaction. Furthermore, the effect of review title on service satisfaction is also found to be significant. This study sheds new light on service quality measurement in airline industry by using an advanced analytical approach to analyze effects of service quality on customer satisfaction. This study also helps managers who want to improve customer satisfaction by providing high quality service in airline industry.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Information Systems Review
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• v.20 no.2
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• pp.111-137
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• 2018

Considering the increase in health insurance benefits and the elderly population of the baby boomer generation, the amount consumed by health care in 2020 is expected to account for 20% of US GDP. As the healthcare industry develops, competition among the medical services of hospitals intensifies, and the need of hospitals to manage the quality of medical services increases. In addition, interest in online reviews of hospitals has increased as online reviews have become a tool to predict hospital quality. Consumers tend to refer to online reviews even when choosing healthcare service providers and after evaluating service quality online. This study aims to analyze the effect of sentiment score of healthcare service quality on hospital rating with Yelp hospital reviews. This study classifies large amount of text data collected online primarily into five service quality measurement indexes of SERVQUAL theory. The sentiment scores of reviews are then derived by SERVQUAL dimensions, and an econometric analysis is conducted to determine the sentiment score effects of the five service quality dimensions on hospital reviews. Results shed light on the means of managing online hospital reputation to benefit managers in the healthcare and medical industry.

• The Journal of Bigdata
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• v.7 no.2
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• pp.113-128
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• 2022

After COVID-19, interest in "sustainable tourism" increased, and the number of tourists who wanted to experience "sustainable tourism" also increased. However, there is a problem that the programs and methods for 'sustainable tourism' are not specific and diverse. In addition, since most of the interests of "sustainable tourism" focus on "environment" and "carbon neutrality," there are not many programs or government policies that can contribute to the community. Therefore, in this study, news data and review data were analyzed to suggest a method for promoting 'sustainable tourism'. First, in this study, major themes of sustainable travel were derived through news big data analysis. Through this analysis, policy themes and events of 'sustainable tourism' were derived. By analyzing news big data related to "sustainable tourism," we would like to analyze the reasons why sustainable travel has not been activated in Korea. Finally, in order to promote sustainable travel in Jeju island, we analyzed user review data of Jeju local currency, and propose a idea to coexist with the local community.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.427-430
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• 2022

추천시스템에서 콜드스타트 문제를 해결하기 위해 다양한 연구들이 진행되고 있다. 하지만, 대부분의 연구는 아직도 사용자 기반의 히스토리 데이터셋을 반드시 필요로 하여, 콜드스타트 문제를 완벽히 해결하지 못하고 있다. 이에 본 논문은 콜드스타트 문제를 완화할 수 있는 기저속성 기반의 추천시스템을 제안한다. 제안하는 방법론을 검증하기 위해, 직접 수집한 한국어 영화 리뷰 데이터셋을 기반으로 성능을 검증하였으며, 평가 결과 제안한 방법론이 키워드와 사용자의 리뷰 점수를 효과적으로 반영한 추천시스템임을 확인할 수 있었고, 데이터 희소성 및 콜드스타트 문제를 완화하여 기존의 텍스트 기반 랭킹 시스템의 성능을 압도하는 것을 확인하였다. 더 나아가 제안된 기저속성 추천시스템은 추론 시에 GPU 컴퓨팅 자원을 요구하지 않기에 서비스 측면에서도 많은 이점이 있음을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.21-36
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• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.321-331
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• 2020

The purpose of this study is to explore the impact of home stay platform on guests' willingness to comment online under the Shared home stay business model. Shared platform of home stay facility in addition to providing a variety of support services, help the landlord to the tenant do offline accommodation services, implementation, trading, will need to take some measures to actively promote the tenant groups to the landlord, the evaluation is objective, effective and sufficient number in order to better promote the sharing credit ecological establishment of home stay facility. In this study, consumers who have used the Shared home stay platform are taken as the research objects. The survey method adopts network questionnaire survey and Likert seven subscales. The statistical software SPSS24.0 program is used to process the data. Firstly, descriptive statistical analysis was conducted, followed by validity analysis and reliability analysis. After the reliability and validity of the questionnaire were determined, correlation analysis and regression analysis were used to verify the proposed hypothesis. The research results of this study are summarized as follows :(1) the usability of platform comment function, guest satisfaction and platform reward have a positive impact on the guest online comment willingness; (2) The credit mechanism of the platform has a positive regulating effect on the process of tenant satisfaction influencing tenant comment intention.