• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.273-287
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• 2024

Purpose - This study conducts the co-occurrence analysis, one of the social network analysis using global OTA's online reviews and grades in order to understand the experience structure of foreign tourists visiting cutural tourism resources in Jeju, Korea. Design/methodology/approach - For this purpose, this study selects 6 cultural tourism resources in Jeju as the study sites, and collects qualitative review data (noun, adjectives, and verb) and quantitative grade data. Findings - The co-occurrence network analysis between words and grade of market and street shows that the grade of 5 appears the most simultaneous with pork, buy, lot, try, fresh, black, food, price, seafood, local, market, good, street, etc. and the grade of 1 connects with small, dish, better, taste, etc. And the co-occurrence network analysis between words and grade of tradition and folklore shows that the grade of 5 appears the most simultaneous with village, place, museum, visit, time, life, culture, women, diver, use, lot, etc. and the grade of 1 connects with minute, spend, room, recommend, honey, etc. Research implications or originality - The above research results are relevant in order to find out the core experience of foreign tourists using online review and grade generated by foreign tourists and use as the important information to develop the strategies related to the planning and management of cultural tourism resources.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.4
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• pp.729-743
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• 2024

This research explores consumer preferences for materials in different clothing product categories, using web-crawling and text mining techniques. Specifically, the study focuses on the material-related terms found in consumer reviews across three distinct product categories: functional clothing, formal shirts, and knit sweaters. Top-selling products within each category were identified on the Naver Shopping website based on the volume of reviews, and the four most-reviewed products were selected. Six hundred reviews per product were analyzed using the Textom big-data analysis software to determine the frequency of material-related mentions and word associations. The analysis utilized two comparative metrics: product category and usage duration. Our findings reveal notable variations in the material preferences mentioned by consumers across different product categories. The study suggests a need to re-evaluate existing standardized review criteria to better reflect consumer interests specific to each product category. Additionally, an increase in material-related terms in reviews over one month indicates the potential importance of extending the duration of product reviews to enhance the accuracy of information that reflects longer-term consumer experiences with material quality.

• Information Systems Review
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• v.24 no.3
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• pp.43-68
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• 2022

With the growing demand for online food delivery (OFD) services via Online to Offline (O2O) platforms, it is required for academic researchers to identify the success factors of OFD businesses. In line with this, this research examines the impact of the core service attributes of a restaurant (hygiene, interactivity, trust,and popularity) on business success in the OFD platform context from the perspective of information asymmetry. Furthermore, the moderating effects of hygiene factor between the core service attributes and the success of restaurants are evaluated. We utilize 1,146 restaurants registered on the largest OFD platform in Korea. The results of this study demonstrate that hygiene (certification), trust (franchise), popularity (favorite) factors have positive impacts on the success of OFD businesses. Moreover, we find that franchise restaurants with high response rates to customer reviews and inquiries achieve higher sales when they have hygiene certifications than those without the certification do. The key findings bear significant contributions to prior literature by empirically substantiating the pivotal role of service quality signals in fostering restaurant success on the OFD platforms. In addition, this study provides business implications for restaurants in O2O platform.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.

• Knowledge Management Research
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• v.24 no.3
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• pp.95-125
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• 2023

As the aviation industry has rapidly been grown, there are more factors for customers to consider when choosing an airline. In response, airlines are trying to increase customer value by providing high-quality services and differentiated experiential value. While early customer value research centered on utilitarian value, which is the trade-off between cost and benefit in terms of utility for products and services, the importance of experiential value has recently been emphasized. However, experiential value needs to be studied in a specific context that fully represents customer preferences because what constitutes customer value changes depending on the product or service context. In addition, customer value has an important influence on customers' decision-making, so it is necessary for airlines to accurately understand what constitutes customer value. In this study, we collected customer reviews and ratings from Skytrax, a website specializing in airlines, and utilized the BERTopic technique to derive factors of customer value. The results revealed nine factors that constitute customer value in airlines, and six of them are related to customer satisfaction. This study proposes a new methodology that enables a granular understanding of customer value and provides airlines with specific directions for improving service quality.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.433-442
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• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.265-274
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• 2021

In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers' purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.453-463
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• 2021

The purpose of this study is to establish the most severe impacts of Covid-19 in the field of the performing arts and to explore countermeasures taken both at home and abroad. This study has been conducted through literature surveys. The conclusion is as follows. First, since the outbreak of Covid-19, theater venues around the world have actively taken part in uploading recorded performances to streaming services. Secondly, these performance visualizings are generally considered as being complimentary goods rather than a substitute for live performance. Thirdly, although more audiences are tuning into watch on-line performances, consumption is concentrated on a few theaters which have a worldwide reputation and a broad range of content. Fourthly, to tackle the impact that Covid-19, the UK government announced a series of job protection schemes in the field of the Arts. In addition, Arts Council England prepared an emergency response package. In Korea, some countermeasures such as government support for artists and cultural establishments have also been implemented. Lastly, some suggestions for the sector. I conclude that there is an need for domestic companies to secure core contents of significant quality and to make strategic alliances with leading overseas performance companies so that they may cooperate together.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.647-656
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• 2017

It is obvious that the sales of smart phones are increasing every year whereas the growth rate is decreasing year by year from 2010 through the investigation of the current situation of the smartphone market. Therefore, the GUI design of smartphones has gradually become the major design difference and selling point. In this research background, the purpose of this paper is to investigate and analyze the relationship between GUI design and user experience which takes the 20 generation smartphone users in China as the research objects, so as to understand the impact of GUI design on user experience. In this paper, five visual elements of GUI design are derived from prior study, and five essential factors of user experience are educed by using online review text analysis and KJ. Finally, this thesis makes a questionnaire survey on the 20 generation smartphone users in China, and analyzes the influence of GUI design on the user experience. Meanwhile, we put forward some suggestions for improving the user experience on the basis of the survey results.