• Journal of Korea Trade
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• v.24 no.6
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• pp.61-81
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• 2020

Purpose - This paper examines how the consumer animosity of partner country influences the purchase of foreign products. We analyzed news sentiment to determine whether Chinese consumer's animosity affect the purchase of the products made in Korea around the time when the U.S. Terminal High Altitude Area Defense missile system was deployed in South Korea. Design/methodology - To measure the tone of Chinese consumer animosity more carefully, we utilized a text mining technique of the Chinese language to read the public's opinion. Using Chinese news paper's editorials of 2015.1-2018.10, we analyzed the sentiment toward Korea and regressed it with Korean export to China. Findings - Empirical results report that Chinese consumers tended to reduce their purchase of consumer goods from Korea when the animosity increased, that is, the sentiments of Chinese news editorials were negative. In contrast, the animosity did not affect the purchase of Korean intermediates or raw materials. We further analyzed the effect by dividing the animosity into three categories; politics, economics, and culture. Among these groups, political news exhibits a unique effect on Chinese purchase on consumer goods from Korea. Originality/value - Existing literature on animosity models has measured the animosity by collecting the consumers' opinions through survey at a given time point, whereas it is measured by analyzing the tone of the press release by sentiment analysis during the time period around the event occurrence in this study.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.61-67
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• 2021

The covid-19 pandemic scenario caused the most extensive economic shocks the world has experienced in decades. Maintaining financial performance and economic stability is essential during the pandemic period. In these conditions, where movement is severely restricted, media consumption is considered to be increasing. The social media platform is one of the media online used by the public as a source of information and also expressing their sentiment, including individual investors in the capital market as social media users. Twitter is one of the social media microblogging platforms used by individual investors to share their opinion and get information. This study aims to determine whether microblogging sentiment investors can predict the capital market during pandemics. To analyze microblogging sentiment investors, we classified sentiment using the phyton text mining algorithm and Naïve Bayesian text classification into level positive, negative, and neutral from November 2019 to November 2020. This study was on 68 listed companies on the Indonesia stock exchange. A Vector Autoregression and Impulse Response is applied to capture short and long-term impacts along with a causal relationship. We found that microblogging sentiment investor has a significant impact on stock returns and volatility and vice-versa. Also, the response due to shocks is convergent, and microblogging investors in Indonesia are categorized as a "news-watcher" investor.

• The Journal of Industrial Distribution & Business
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• v.11 no.12
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• pp.7-15
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• 2020

Purpose: This study aims to explore the feasibility of expanding complex wards to provide efficient hospital management and high-quality medical services to local residents of Gangneung Medical Center (GMC). Research Design, Data and Methodology: There are four research designs to achieve the research objectives. We analyzed Big Data for 3 months on Social Network Services (SNS). A questionnaire survey conducted on 219 patients visiting the GMC. Surveys of 20 employees of the GMC applied. The feasibility to expand the GMC ward measured through Focus Group Interview by 12 internal and external experts. Data analysis methods derived from various surveys applied with data mining technique, frequency analysis, and Importance-Performance Analysis methods, and IBM SPSS statistical package program applied for data processing. Results: In the result of the big data analysis, the GMC's recognition on SNS is high. 95.9% of the residents and 100.0% of the employees required the need for the complex ward extension. In the analysis of expert opinion, in the future functions of GMC, specialized care (△3.3) and public medicine (△1.4) increased significantly. Conclusion: GMC's complex ward extension is an urgent and indispensable project to provide efficient hospital management and service quality.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1218-1221
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• 2011

전 세계적으로 인터넷 보급률이 높아지고 전자상거래가 활발해지면서 직접 발품을 팔아가며 보다 비싼 가격에 물건을 구입하는 일보다는 인터넷몰을 이용하여 제품을 구매하는 일이 늘어나고 있다. 특히 인터넷몰 중에서도 의류를 판매하는 인터넷 쇼핑몰은 새로운 산업으로 각광받으며 하루가 다르게 새로운 사이트가 우후죽순 생겨나고 있다. 자리에 앉아 직원의 직접적인 홍보에 대한 부담을 갖지 않고 차분히 제품을 싼 가격에 구입할 수 있다는 이점을 갖고 있는 인터넷 쇼핑몰이지만 직접 제품을 눈으로 확인하지 못하기 때문에 실제 제품과 동일한 제품인지 조작된 사진인지의 여부를 확인하기 힘들고 직원과의 상담 역시 전화나 인터넷을 통해 진행되기 때문에 환불이나 교환과 같은 의사소통에 문제가 생기고는 한다. 이에 본 논문에서는 오피니언 마이닝을 통해 웹 서버에 저장되어 있는 수많은 쇼핑몰의 리뷰를 정리하고 각 사이트의 서비스, 품질 등에 대한 평가를 카테고리 별로 분석하여 소비자가 현명하고 효율적인 소비 결정을 내릴 수 있도록 도울 방법을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1184-1187
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• 2011

스마트폰 시장이 커지면서, 사람들이 하루에 업로드하고 다운로드하는 어플리케이션의 수 또한 급격히 증가하고 있다. 앱스토어와 안드로이드마켓에 등록된 어플리케이션의 종류는 어마어마하며, 사람들은 자신의 생활을 편리하게 해줄 어플리케이션 혹은 재미를 위한 어플리케이션을 다운로드하고자 한다. 하지만 현재 어플리케이션에 대한 평가는 점수로만 이루어져있기 때문에 어느 부분에서 뛰어난지, 어떤 부분의 기능이 떨어지는지는 사용자가 알 수 없고, 특정 기능을 중요시하는 사용자일 경우 별점이 높아도 해당기능이 만족스럽지 않으면 만족감의 정도는 대단히 떨어지게 된다. 그러면 다른 어플리케이션을 받아 같은 작업을 반복해야하는데, 이 경우가 반복될 경우 비용적인 문제뿐만 아니라 사용자에게 매우 번거로운 일이다. 따라서 본 논문에서는 기존 사용자들이 자신이 사용한 어플리케이션에 대해 작성한 후기를 오피니언 마이닝 기술을 적용시켜 각 키워드별, 즉 속성별로 평가하고 긍정/부정 여부를 데이터베이스에 저장하여, 해당 어플리케이션을 검색한 미래의 어플리케이션 사용자에게 시각적으로 정보를 알려주어 사용자의 수고를 덜어주고자 한다. 어플리케이션 다운로드가 매우 단순한 작업이지만, 다운로드 수가 많기 때문에 본 논문의 제안을 적용한다면 비용을 절감시켜 줄 뿐만 아니라 매우 효율적인 작업이 될 것이라 기대한다.

• Steel and Composite Structures
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• v.47 no.4
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• pp.451-454
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• 2023

Landslides are one of the natural disasters that cause a lot of financial and human losses every year It will be all over the world. China, especially. The Mainland China can be divided into 12 zones, including 4 high susceptibility zones, 7 medium susceptibility zones and 1 low susceptibility zone, according to landslide proneness. Climate and physiography are always at risk of landslides. The purpose of this research is to prepare a landslide hazard map using the Hierarchical Analysis Process method. In the GIS environment, it is in a part of China watershed. In order to prepare a landslide hazard map, first with Field studies, a distribution map of landslides in the area and then a map of factors affecting landslides were prepared. In the next stage, the factors are prioritized using expert opinion and hierarchical analysis process and nine factors including height, slope, slope direction, geological units, land use, distance from Waterway, distance from the road, distance from the fault and rainfall map were selected as effective factors. Then Landslide risk zoning in the region was done using the hierarchical analysis process model. The results showed that the three factors of geological units, distance from the road and slope are the most important have had an effect on the occurrence of landslides in the region, while the two factors of fault and rainfall have the least effect The landslide occurred in the region.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.