• The Journal of Information Systems
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• v.31 no.2
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• pp.1-18
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• 2022

Purpose Customer review is a major factor in choosing certain restaurants. This study investigates the key factors affecting customer's evaluation about restaurants. With the recent intensification of competition among restaurants in the service industry, the analysis results are expected to provide in-depth insights for enhancing customer experiences. Design/methodology/approach We collected information and reviews provided at the restaurants in the Kakao Map platform. The information collected is based on the information of 3,785 restaurants in Daegu registered on Kakao Map. Based on the information collected, seven independent variables, including number of rating registered, number of reviews, presence or absence of safe restaurants, presence or absence of a posting about holding facilities, presence or absence of a posting about business hours, presence or absence of a posting about hashtags, and presence or absence of break times, were used. Dependent variable is restaurant rating. Multiple regression between independent variables and restaurant rating was carried out. Findings The results of the study confirmed that number of rating registered, presence or absence of a posting about business hours, and presence or absence of a posting about hash tags have an positive effects on the restaurant rating. The number of reviews had a negative effect on the restaurant rating. In addition, in order to confirm the role of customer's reviews, we carried out LDA topic modeling. We divided the topics into the positive review and the negative reviews.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.1-20
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• 2022

As the global business environment is rapidly changing due to the 4th industrial revolution, new jobs that did not exist before are emerging. Among them, the job that companies are most interested in is 'Data Scientist'. As information and communication technologies take up most of our lives, data on not only online activities but also offline activities are stored in computers every hour to generate big data. Companies put a lot of effort into discovering new opportunities from such big data. The new job that emerged along with the efforts of these companies is data scientist. The demand for data scientist, a promising job that leads the big data era, is constantly increasing, but its supply is not still enough. Although data analysis technologies and tools that anyone can easily use are introduced, companies still have great difficulty in finding proper experts. One of the main reasons that makes the data scientist's shortage problem serious is the lack of understanding of the data scientist's job. Therefore, in this study, we explore the job competencies of a data scientist by qualitatively analyzing the actual job posting information of the company. This study finds that data scientists need not only the technical and system skills required of software engineers and system analysts in the past, but also business-related and interpersonal skills required of business consultants and project managers. The results of this study are expected to provide basic guidelines to people who are interested in the data scientist profession and to companies that want to hire data scientists.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.223-229
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• 2022

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.19-32
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• 2023

Using extensive job posting big data, this study investigates the industrial and locational characteristics of decent work from the supply side. The analysis revealed that manufacturing is pivotal in supplying decent work, accompanied by a stark regional disparity, most notable in the Seoul Metropolitan Statistical Area (MSA), which constitutes nearly half of all decent work opportunities. The study further uncovered that the distribution of decent work varies significantly across MSAs, with a pronounced inclination towards a higher supply in peripheral rather than central areas. These findings bring to light the critical need for policies that bolster manufacturing, aiming to enhance the availability of high-quality jobs and to bridge the job quality gap between the Seoul MSA and other regions. Moreover, the results emphasize the necessity for customized job supply strategies in each MSA, prioritizing strategies that account for the proximity between workplaces and living areas in the job supply process.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.169-175
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• 2001

Bridge load rating calculations provide a basis for determining the safe load capacity of bridge. Load rating requires engineering judgement in determining a rating value that is applicable to maintaining the safe use of the bridge and arriving at posting and permit decisions. Load testing is an effective means in calculating the rating value of bridge. In Korea, load carrying capacity of bridge is modified by response modification factor that is determined from comparisons of measured values and analysis results. The response modification factor may be corrupted by vehicle location error that is defined as the gap of test vehicle location between load testing and analysis. In this study, the effects of vehicle location error to structural response and response modification factor are investigated, and a new method for evaluating response modification factor is proposed. The random data analysis shows that the proposed method is less sensitive to vehicle location error than the present method.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.13-24
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• 2022

SNS has become a very close service to our daily life. As marketing is done through SNS, places often called hot places are created, and users are flocking to these places. However, it is often crowded with a large number of people in a short period of time, resulting in a negative experience for both visitors and service providers. In order to improve this problem, it is necessary to recognize the congestion level, but the method to determine the congestion level in a specific area at an individual level is very limited. Therefore, in this study, we tried to propose a system that can identify the congestion level information and the characteristics of visitors to a specific tourist destination by using the data on the SNS. For this purpose, posting data uploaded by users and image analysis were used, and the performance of the proposed system was verified using the Naver DataLab system. As a result of comparative verification by selecting three places by type of tourist destination, the results calculated in this study and the congestion level provided by DataLab were found to be similar. In particular, this study is meaningful in that it provides a degree of congestion based on real data of users that is not dependent on a specific company or service.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.258-266
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• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.701-711
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• 2017

This research aimed to understand selfie behavior in social networking sites (SNSs). The research was conducted on the basis of the functional theories of attitude, verified self-presentation attitude, and self-expression attitude that affect selfie behaviors (i.e., taking selfies, posting selfies, and taking selfies for fashion product exposure). The moderating effect of satisfaction toward one's appearance was identified. The participants of the study were SNS users aged 20-30 years who had posted selfies in the past month. A survey was performed using an online panel of an international survey firm. The data were analyzed using hierarchical regression analysis on SPSS 22.0. Results corroborated that self-expression attitude affected the number of selfies taken but not the number of selfies posted and those uploaded for fashion product exposure. Self-presentation attitude exerted a significant effect on the number of selfies posted and those uploaded for fashion product exposure. When satisfaction toward one's appearance was high, self-presentation attitude increased the influence of the behaviors of posting selfies and uploading selfies for fashion product exposure. Self-expression attitude also significantly influenced the number of selfies taken due to the moderating effect of satisfaction toward one's appearance. This research was made meaningful by its quantitative analysis of selfie behavior in SNSs. The results confirmed the different functions of attitudes affecting selfie behavior. With the improved understanding of selfie behavior obtained from this research, Social Media marketing may be carried out in various industrial fields in the future.