• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.139-146
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• 1996

The estimation technique of psychophysical magnitude Is useful tool which measures to subjective feeling or opinion of human. This paper Introduces properties of the measured data, scales(nominal. ordinal. Interval. and ratio scale). and right analyzing methods of the measured data.

• Design Convergence Study
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• v.15 no.2
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• pp.347-368
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• 2016

Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• East Asian mathematical journal
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• v.37 no.5
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• pp.599-612
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• 2021

In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.

• Journal of Korean Society of Rural Planning
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• v.16 no.3
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• pp.43-49
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• 2010

This study generalized and systemized the unit-project items of 176 project districts for the rural village comprehensive development projects (RVCDP). This paper surveyed opinions of III answerers (7 specialists, 43 agents of Korea Rural Community corporation, and 61 agents of local government of cities and counties) in order to determine the classification system and their corresponding weighting values of the project items using analytic hierarchy process (AHP) method. From the results extracted by project plans of 176 project districts for 5 years from 2004 to 2008, this study decided a hierarchical system for unit-project items of RVCDP by AHP method, which consisted of three steps: 4 items for 1st step, 13 items for 2nd step, and 52 items for 3rd step. The first step contains 4 items of Strength of Rural-urban Exchange & Regional Capability (RURC), Green-income Infrastructure & Facility (GIF), Culture-health-welfare Facility, and Eco-environment & Landscape facility (ELF). In the survey of weighting values with AHP method, the analysis result for the first step showed that in opinion of specialists, GIF is more important than the others while in opinion of the other agents, RURC is more important. In the second step, Product Facility is more important in the specialists, whereas Strength of Resident Capability is the most important in the other agents. Analyzed unit project items as the third step, all answerers evaluated that the Education and Excursion for Rural Resident Capability has the highest weighting values.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.4
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• pp.309-326
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• 2011

There have been several milestones in the history of library classification but most of the schemes failed to meet the new challenges in the organisation of information. Dr. S. R. Ranganathan tried to revolutionise the whole thinking on classificatory approach, when he devised the Colon Classification (CC) in 1933. He developed the Colon Classification scheme with a sound theoretical background based on normative principles, Five laws of Library science, canons, etc. One important feature of CC is that, its use is not confined to information storage and retrieval alone. This paper presents an over view of different editions of the CC highlighting the salient features of the editions. Further the implication of Five Laws of Library Science has been described. The authors stressed that the features of such as greater hospitality, specificity and mixed notation has paved the way to design and develop the depth schedules on various micro level subjects and so far about 130 micro schedules have been published. The impressions by the leading LIS professionals during and after Ranganathan's time have been highlighted. The authors expressed the fear that when the library world would see the complete version of the seventh edition of CC? It may be due to lack of institutional support engaging in the research or financial constraints. The authors are of the opinion that any scheme to flourish needs a sound research body to bring out the revised editions as done in the case of Dewey Decimal Classification. The relevance of the CC in the contemporary world of Librarianship is discussed. Finally concludes that CC needs to be resuscitated as it is a precious national heritage; and still a force for the management of libraries.

• The Korean Journal of Pain
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• v.35 no.1
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• pp.4-13
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• 2022

Changes in diagnostic criteria, for example, the various International Classification of Headache Disorders criteria, would lead to changes in the outcomes of epidemiological studies. International Classification of Headache Disorders-1 was based mainly on expert opinion, yet most of the diagnostic criteria were reliable and valid, but it did not include chronic migraine. In its second version, the classification introduced chronic migraine, but this diagnosis resembled more a high-frequency migraine rather than the actual migraine transformation process. It also introduced medication overuse headache, but it necessitated analgesic withdrawal and subsequent headache improvement to be diagnosed as such. Hence patients having medication overuse headache could only be diagnosed in retrospect, which was an awkward situation. Such restrictive criteria for chronic migraine and medication overuse headache omitted a high proportion of patients. International Classification of Headache Disorders-3 allows a diagnosis of medication overuse headache due to combination analgesics if taken for at least 10 days per month for more than three months. Hence the prevalence rate of medication overuse headache and chronic migraine can increase compared to the previous version of the headache classification. Different criteria have been used across studies to identify chronic migraine and medication overuse headache, and therefore the information acquired from previous studies using earlier criteria becomes uncertain. Hence much epidemiological research would need to be interpreted cautiously or repeated with the most updated criteria, since the subjects in studies that apply the latest criteria may be phenotypically different from those in older studies.

• Journal of Korean Elementary Science Education
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• v.26 no.4
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• pp.395-406
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• 2007

The purpose of this study was to investigate the characteristics of 2nd grade students' interaction in the classification activities of LTTS. For the purposes of this study, three heterogeneous groups, chosen by cognitive level, were selected. The students' interactions were audio/video taped and classified as either cognitive or affective interaction. The results of this study are as follows. In the cognitive interactions, the frequency and quality of the functions of 'questions' and 'making suggestions' were higher than those of 'Responses' and 'Receiving opinions'. In the affective interactions, the frequency of 'induction' and 'dissatisfaction' was higher than that of the other types. The frequency and quality of interactions of students in both the early and mid concrete stage were higher than those of students in the transitional stage. Qualitatively higher-level interactions such as 'making suggestions' and positive interactions such as 'induction' to induce students who were passive in activities were made by the students at higher cognitive levels. However, the low-level of interaction in suggesting their opinion to the constituent's suggestion and 'dissatisfaction' with student in transition period who were passive in activity influenced group working negatively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.440-443
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• 2005

The wide spread of internet business recently necessitates recommendation systems which can recommend the most suitable product fur customer demands. Currently the recommendation systems use content-based filtering and/or collaborative filtering methods, which are unable both to explain the reason for the recommendation and to reflect constantly changing requirements of the users. These methods guarantee good efficiency only if there is a lot of information about users. This paper proposes an algorithm called 'demand articulate & integration' which can perceive user's continuously varying intents and recommend proper contents. A method of knowledge classification which can be applicable to this algorithm is also developed in order to disassemble knowledge into basic units and articulate indices. The algorithm provides recommendation outputs that are close to expert's opinion through the tracing of articulate index. As a case study, a knowledge base for heritage information is constructed with the expert guide's knowledge. An intelligent recommendation system that can guide heritage tour as good as the expert guider is developed.