• 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.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.97-104
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• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• Journal of Korea Trade
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• v.24 no.4
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• pp.34-48
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• 2020

Purpose - The rapid rise of e-commerce enterprises has led to the development of the logistics industry. At the same time, some enterprises are motivated by the interests to start reducing costs and inputs, which on the contrary leads to low quality of service, thus reducing customer satisfaction. In recent years, vicious competition, violent express delivery and lack of professionalism in the logistics market have led to high annual customer complaint rate, which has resulted in the company losing many loyal customers, but also unable to obtain new customers. Therefore, to pay attention to and understand the psychological needs of customers and improve the quality of logistics distribution service has become a pressing problem for Every express company. Design/methodology - By analyzing the problems existing in logistics distribution of express companies, this paper explores various factors affecting customer satisfaction and takes consumer sentiment as a mediating variable. Through questionnaires to collect relevant data, put forward hypotheses for empirical analysis, use two different software including SPSS 21.0 and AMOS 21.0 to analyze the information, draw conclusions and make recommendations. Findings - According to the above research results, the reliability, convenience, efficiency, professional can have a positive impact on customer satisfaction through the mediating effect of their sentiment, convenience and professional on consumer sentiment and satisfaction are more significant. Originality/value - This paper the establishment of distribution service indicators related to customer satisfaction and empirical analysis can not only enrich and supplement the distribution service quality indicator system studied by the former, but also provide a theoretical basis for future research.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.347-356
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• 2023

In the recent war between Ukraine and Russia, low-orbit satellite communication played a major role, and Korea laid a foothold for low-orbit satellite communication services with the successful launch of Nuri in May 2023 and entered a full-scale civilian space age competition. In order to create an ecosystem for ICT digital transformation and data convergence in the field of low-orbit satellite communication, this paper conducts user sentiment analysis by importing posts from Reddit, one of the world's SNS, and extracts need-related sentences through need mining to identify user needs, performs topic modeling to classify topics, and prepares an action plan according to these topics. We hope that this study will be used as a policy resource for the development and innovation of new business models in the field of low-orbit satellite communication, bridging the digital information gap and solving social problems, contributing to sustainable digital transformation and enhancing soft power.

• Journal of the Korean Society of Food Culture
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• v.31 no.2
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• pp.170-177
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• 2016

This study carried out empirical analysis of the effect on conspicuous consumption and purchase intention by social face sensitivity of customers who have eaten at luxury restaurants. Adult male and female customers aged 20~60 years who lived in Seoul and who had experience eating at luxury restaurants were selected as survey participants. The results of this study are as follow. First, social face sensitivity factor had a significant effect on preference for famous brands and seeking fashion. On the other hand, among social face sensitivity factors, shame consciousness had a significant effect on other-oriented conspicuous consumption. Thus, the hypothesis was partially accepted. Second, among social face sensitivity factors, other-conscious social face had a significant effect on purchase intention. Thus, the hypothesis was partially accepted. Third, preference for famous brand and seeking fashion had a significant effect on purchase intention. However, other-oriented conspicuous consumption tendency had no effect on purchase intention. Thus, the hypothesis was partially accepted. If studies on various consumption sentiment variables continue to be made, these may be usefully utilized for establishing marketing strategies of companies.

• The Journal of Bigdata
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• v.1 no.2
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• pp.35-44
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• 2016

Today there is a fierce competition between social commerce and multi-channel distribution in korea and it is need to do comparative analysis about success factors between social commerce and multi-channel distribution. Unlike the other studies that have only used survey method, this study analyzed the success factors between social commerce and multichannel distribution using text mining techniques. We expect that the result of the study not only gives the practical implication for making the competition strategy of the retailers but also contributes to the diverse extension research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.543-546
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• 2017

Recently, people are using the data analysis in order to follow the new trend in information and communication technology. Media plays an important role to expand the new issue in our society, especially affected to establish social awareness about science and technology. So, We find some major technologies (Machine Learning & Blockchains) of future communication and information based on the 200 news articles through two data analysis methods such as keyword analysis and sentiment analysis. We look forward this paper to constantly develop the technology of information and communication as the guiding frame of the new scientific world.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.66-77
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• 2021

Companies widely use survey to identify customer requirements, but the survey has some problems. First of all, the response is passive due to pre-designed questionnaire by companies which are the surveyor. Second, the surveyor needs to have good preliminary knowledge to improve the quality of the survey. On the other hand, text mining is an excellent way to compensate for the limitations of surveys. Recently, the importance of online review is steadily grown, and the enormous amount of text data has increased as Internet usage higher. Also, a technique to extract high-quality information from text data called Text Mining is improving. However, previous studies tend to focus on improving the accuracy of individual analytics techniques. This study proposes the methodology by combining several text mining techniques and has mainly three contributions. Firstly, able to extract information from text data without a preliminary design of the surveyor. Secondly, no need for prior knowledge to extract information. Lastly, this method provides quantitative sentiment score that can be used in decision-making.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.294-300
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• 2021

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.13-30
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• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.