• Journal of the Korean Regional Science Association
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• v.34 no.4
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• pp.19-34
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• 2018

The purpose of this study is to analyze the seasonal issues using the local newspaper articles with the keyword beach from 2004 to 2017. Topic modeling and Time series regression analysis based on open source programs were performed for analysis. Topic modeling results showed 35 topics in spring, 47 topics in summer, 36 topics in autumn and 35 topics in winter. The common themes were 'beaches', 'festivals and events', 'accident and environmental issues', 'tourism', 'development and sale', 'administration and policy' and 'weather'. Time series regression analysis showed in the spring, 5 Hot-Topics and 2 Cold-Topic were found out of the 35 topics. In the summer, 6 Hot-Topics and 3 Cold-Topic were found out of the 47 topics. In the autumn, 4 Hot-Topics and 3 Cold-Topic were found out of the 36 topics. In the winter, 3 Hot-Topics and 3 Cold-Topic were found out of the 35 topics. And for each season, topics that do not fall into the Hot-Topic and Cold-Topic are classified as Neutral-Topic. In this study if seasonal uses are different such as beaches are deemed that seasonal topic modeling for analysis of regional issues will yield more useful results and enable detailed diagnosis.

• 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.

• Korea Trade Review
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• v.45 no.3
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• 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.

• 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.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.421-431
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• 2016

Over the last four decades, industrial engineering (IE) research in Korea has continued to evolve and expand to respond to social needs. This paper aims to identify research topics in IE research and explore their dynamic changes over time. The topic modeling approach, which automatically discovers topics that pervade a large and unstructured collection of documents, is adopted to identify research topics in domestic IE research. 1,242 articles published from 2001 to 2015 in two IE journals issued by the Korean Institute of Industrial Engineers were collected and their English abstracts were analyzed. Applying the Latent Dirichlet Allocation model led us to uncover 50 topics of domestic IE research. The top 10 most popular topics are revealed, and topic trends are explored by examining the dynamic changes over time. The four topics, technology management, financial engineering, data mining (supervised learning), efficiency analysis, are selected as hot topics while several traditional topics related with manufacturing are revealed as cold topics. The findings are expected to provide fruitful implications for IE researchers.

• The Bulletin of The Korean Astronomical Society
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• v.30 no.1
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• pp.22.1-22.1
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• 2005

• Journal of the Microelectronics and Packaging Society
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• v.27 no.2
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• pp.53-58
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• 2020

Analysis of electric semiconductor packaging technology for electric vehicles was performed. Topic modeling using LDA technique was performed by collecting valid patents by deriving valid patents. It was classified into 20 topics, and the definition of technology was defined through extracted words for each topic. In order to analyze the trend of each topic, the trend of power semiconductor packaging technology was analyzed by deriving hot and cold topics by topic through regression analysis on frequency by year. The package structure technology according to the withstand voltage, the input/output-related control technology and the heat dissipation technology were derived as the hot topic technology, and the inductance reduction technology was derived as the cold topic technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.