• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.690-693
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• 2022

In this study, we used wine data to perform correlation analysis on factors that affect wine quality, and predicted wine quality standards based on the results. The dataset used in this study used data from 1599 red wines and 4898 white wines produced in Vinho verde, Portugal, for a total of 6497. The variable items are 12 kinds of component variables that represent wine components through physical and chemical analysis tests, a total of 1599 observations, and a total of one of the representative wines of the three major wine producing regions in the world (France, Italy, Spain). Added 3 pieces. Analysis was made by applying national climate change data.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.231-238
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• 2021

In this paper, we suggest a method of analysis for daily food delivery & consumption trends through big data of the post-Covid-19 era. Through analysis of big data and the database system, four analyzed factors, excluding weather, was proved to have significant correlation with delivery sales for 'Baedarui Minjok' of a catering delivery application. The research found that KBS, MBC and SBS Media showed remarkable results in food delivery & consumption sales soaring up to about 60 percent increase on the day after the Covid-19 related new article was issued. In addition, it proved that mobile media and web surfing were the main factors in increasing sales of food delivery & consumption applications, suggesting that viral marketing and emotional analysis by crawling data from SNS used by Millennials might be an important factor in sales growth. It can contribute the companies in the economic recession era to survive by providing the method for analyzing the big data and increasing their sales.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.506-509
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• 2017

Opinion mining is a way to analyze the emotions in the text and is used to identify the emotional state of the author and to find out the opinions of the public. As you can analyze individual emotions through opinion mining, if you analyze the text by region, you can find out the emotional state you have in each region. The regional sentiment analysis can obtain information that could not be obtained from personal sentiment analysis, and if a certain area has emotions, it can understand the cause. For regional sentiment analysis, we need text data created by region, so we need to collect data through Twitter crawling. Therefore, this paper designs a Twitter data collection system for regional sentiment analysis. The client requests the tweet data of the specific region and time, and the server collects and transmits the requested tweet data from the client. Through the latitude and longitude values of the region, it collects the tweet data of the area, and it can manage the text by region and time through collected data. We expect efficient data collection and management for emotional analysis through the design of this system.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.15-29
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• 2021

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.