• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.217-225
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• 2017

Recently, the interest in predicting hot topics has grown significantly as it has become more important to find and analyze meaningful information from a large amount of data flowing in social networking services. Existing hot topic detection schemes do not consider a temporal property, so they are not suitable to predict hot topics that are rapidly issued in a changing society. This paper proposes a hot topic prediction scheme that uses a modified TF-IDF in social networking environments. The modified TF-IDF extracts a candidate set of keywords that are momentarily issued. The proposed scheme then calculates the hot topic prediction scores by assigning weights considering user influence and professionality to extract the candidate keywords. The superiority of the proposed scheme is shown by comparing it to an existing detection scheme. In addition, to show whether or not it predicts hot topics correctly, we evaluate its quality with Korean news articles from Naver.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.24-36
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• 2015

Recently, interests in detecting hot topics have been significantly growing as it becomes important to find out and analyze meaningful information from the large amount of data which flows in from social network services. Since it deals with a number of random writings that are not confirmed in advance due to the characteristics of SNS, there is a problem that the reliability of the results declines when hot topics are predicted from the writings. To solve such a problem, this paper proposes a high reliable hot topic prediction scheme considering user influences in social networks. The proposed scheme extracts a set of keywords with hot issues instantly through the modified TF-IDF algorithm based on Twitter. It improves the reliability of the results of hot topic prediction by giving weights of user influences to the tweets. To show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation. Our experimental results show that our proposed method has improved precision and recall compared to the existing method.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.71-72
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• 2015

소셜 네트워크의 발달로 대량의 데이터로부터 원하는 정보를 빠르게 분석하고 유의미한 정보를 찾아내는 것이 중요해지면서 핫토픽 검출에 대한 관심이 증가하고 있다. 본 논문에서는 단어의 출현 빈도수뿐만 아니라 사용자 영향력을 종합적으로 고려하여 이를 기반으로 트윗에 가중치를 부여함으로써 검출 결과의 신뢰성을 향상 시킬 수 있는 핫 토픽 검출 기법을 제안한다.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.273-289
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• 2019

The goal of this study is to analyze topics discussed in academic papers on multiculture in Korea to figure out research trends in the field. In order to do topic analysis, LDA (Latent Dirichlet Allocation)-based topic modeling methods are employed. Through the analysis, it is possible to track topic changes in the field and it is found that topics related to 'social integration' and 'multicultural education in schools' are hot topics, and topics related to 'cultural identity and nationalism' are cold topics among top five topics in the field.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.730-733
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• 2015

대용량의 인터넷 뉴스 데이터로부터 유용한 정보를 찾기 위해 연관 키워드, 핫 키워드 분석과 같은 다양한 분석 기술들이 연구되고 있다. 기존의 토픽 모델 기반의 기법은 키워드들간의 연관성을 제대로 표현하지 못하여 마이닝한 연관 키워드와 핫 키워드의 정확도가 낮은 문제점이 있다. 최근, 뉴스 데이터를 뉴스 내의 단어를 버텍스로, 같은 문장내의 단어들을 에지로 연결하는 그래프 기반의 모델링기법이 연구되었다. 이러한 뉴스 그래프 DB에서 그래프 마이닝 기술을 적용하면 연관 키워드, 핫 키워드를 마이닝 할 수 있다. 본 논문은 그래프 마이닝 기술 기반의 효과적인 뉴스 데이터 분석 기술을 제안한다. 실제 뉴스 데이터를 통해 마이닝한 유용한 뉴스 그래프 패턴들을 보이고 뉴스 데이터 분석에 효과적으로 활용될 수 있음을 보인다.

• Smart Media Journal
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• v.11 no.8
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• pp.9-20
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• 2022

With the rise of the metaverse industry worldwide, relevant national strategies and nurturing systems have been prepared in Korea. As the complexity of policies increases, the importance of establishing data-based policymkaing is growing, and studies diagnosing national R&D trends in the metaverse field are still lacking. Therefore, this paper collected NTIS national R&D information for 9,651 R&D projects promoted from 2002 to 2020. And this study looked at the current status and identified major topics based on the topic modeling, and considered time-series changes in the topics. Eleven major topics of R&D tasks in the metaverse field were derived, hot topics were service/content/platform development and medical/surgical fields of application fields, and cold topics were urban/environment/spatial information fields. Strategic R&D Management, metaverse-related laws, and institutional studies were proposed as policy directions.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.17-26
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• 2021

For the past half century, the Journal of the Korean Landscape Architecture has been leading the landscape architecture research and industry inclusively. In this study, abstracts of 1,802 articles were collected and analyzed with topic modeling and network analysis method. As a result of this paper, a total of 27 types of subjects were identified. Health and healing in the field of environmental psychology, garden and aesthetics, participation and community, modernity, place and placenness, microclimate, tourism and social equity also have been continued as important research area in this journal. Modernity, community and urban regeneration is hot topics and ecological landscape related topics were cold topics. Although there was a difference by subject, the variability of the research subjects appeared after the 2000s. In Network analysis, it shows that 'Park' is a representative keyword that can symbolize the journal, and 'landscape' is also important a leading area of the journal. Looking at the overall structure of the network, it can be seen that the journal conducts research on 'utilizing', 'using', and creating 'park', 'landscape', and 'space'. This study is meaningful in that it grasped the overall research trend of the journal by using topic modeling and network analysis of text mining.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.207-210
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• 2018

인플루언서(Influencer)란 소셜 미디어에서 유난히 많은 영향력과 파급효과를 가지고 오는 사람들을 말하며, 이들이 만들어내는 콘텐츠는 이제는 자신들의 브랜딩을 넘어선 커머스(Commerce) 효과를 발휘하고 있다. 본 논문에서는 소셜 웹 그리고 공공데이터를 중심으로 뷰티 빅데이터와 방송 콘텐츠 빅데이터를 수집하고 분석하여 상호 상관성에 기반하여 화장품 관련 기업에서 CRM(Customer Relation Management), PLM(Product Lifecycle Management, SCM(Supply Chain Management System) 등의 경영정보시스템과 연계한 뷰티 분야에 최적화된 통합 경영정보시스템을 제안한다.