• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.265-284
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• 2022

This study applied dynamic topic modeling on titles and abstracts of 55,442 academic papers in 85 SSCI journals from 2001 to 2020 in order to analyze research topic trend in library and information science. The analysis revealed four major themes of library management, informatics, library service, and library system in 10 major topics. The results also showed subtopics in information science and library management topic areas to change over time, while library service remained stable over 20 years to establish itself as a robust topic. In addition, medical information emerged as a significant sub-topic of informatics, thus exemplifying the interdisciplinary characteristics of library and information science field.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.329-342
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• 2020

In recent years, emotional text classification is one of the essential research contents in the field of natural language processing. It has been widely used in the sentiment analysis of commodities like hotels, and other commentary corpus. This paper proposes an improved W-LDA (weighted latent Dirichlet allocation) topic model to improve the shortcomings of traditional LDA topic models. In the process of the topic of word sampling and its word distribution expectation calculation of the Gibbs of the W-LDA topic model. An average weighted value is adopted to avoid topic-related words from being submerged by high-frequency words, to improve the distinction of the topic. It further integrates the highest classification of the algorithm of support vector machine based on the extracted high-quality document-topic distribution and topic-word vectors. Finally, an efficient integration method is constructed for the analysis and extraction of emotional words, topic distribution calculations, and sentiment classification. Through tests on real teaching evaluation data and test set of public comment set, the results show that the method proposed in the paper has distinct advantages compared with other two typical algorithms in terms of subject differentiation, classification precision, and F1-measure.

• The Journal of Information Systems
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• v.18 no.3
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• pp.327-349
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• 2009

Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.128-136
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• 2016

It is impossible for any human being to analyze the more than 500 million tweets that are generated per day. Lexical ambiguities on Twitter make it difficult to retrieve the desired data and relevant topics. Most of the solutions for the word sense disambiguation problem rely on knowledge base systems. Unfortunately, it is expensive and time-consuming to manually create a knowledge base system, resulting in a knowledge acquisition bottleneck. To solve the knowledge-acquisition bottleneck, a topic signature is used to disambiguate words. In this paper, we evaluate the effectiveness of various features of newspapers on the topic signature extraction for word sense discrimination in tweets. Based on our results, topic signatures obtained from a snippet feature exhibit higher accuracy in discriminating company names than those from the article body. We conclude that topic signatures extracted from news articles improve the accuracy of word sense discrimination in the automated analysis of tweets.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.514-536
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• 2022

Online reviews offer valuable information to businesses by reflecting consumer experiences about their products and services. Two crucial aspects of online reviews are the topics consumers choose to address, and the sentiments expressed in their reviews. Building upon previous literature that shows online reviews are context-dependent, we employ the Expectation-Confirmation Theory (ECT) to examine the impact of topic distribution on review sentiment in South Korea and the U.S. during pre- and post-pandemic periods. After applying a topic modeling to Airbnb app review data, we measure the contribution of each topic on review sentiment using SHAP values. Our results indicate variations in topic distribution trends between 2018 and 2021. In addition, the order and magnitude of topics' impact on review sentiment change between pre- and post-pandemic periods for both countries. This study can help businesses understand how topics and sentiments associated with their products and services changed after the pandemic and thus identify areas of improvement.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.873-885
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• 2022

The particulate matter (PM) has emerged as a hot topic around the world as it has been reported that PM is related to an increase in mortality and prevalence rates. In South Korea, the importance of PM has been recognized since the late 1990s, and various studies on PM have been conducted. This study investigated the PM research topics and trends for papers (D=2,764) published in Research Information Sharing Service (RISS) using topic modeling based on Latent Dirichlet Allocation (LDA). As a result, a total of 10 topics were identified in the whole papers, and the PM research topics were classified as 'PM reduction (Topic 1)', 'Government policy and management (Topic 2)', 'Characteristics of PM (Topic 3)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', 'Bio (Topic 6)', 'Traffic (Topic 7)', 'Asian dust (Topic 8)', 'Indoor PM (Topic 9)', 'Human risk (Topic 10)'. In particular, the proportion of papers on topics 'Government policy and management (Topic 2)', 'PM model (Topic 4)', 'Environmental education (Topic 5)', and 'Bio (Topic 6)' to the toal number of papers increased over time (linear slope > 0). The results of this study provide the new literature review methodology related to particulate matter and the history and insight.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.119-128
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• 2009

Many researchers have argued about the relationship between information structure and intonation. Their results can be summarized in three main points: the intonation of topic and focus in English information structures is implemented as i) a pitch accent, ii) a tune (a pitch accent + (an) edge tone(s)), or iii) a boundary tone. The purpose of this paper is to study various intonational patterns of topic and focus in English information structures, using natural conversations. In this paper, the types of topics and foci are divided, based on contrastiveness. The topics are classified as five non-contrastive and four contrastive topics. The foci are classified as neutral focus, informational focus, and contrastive focus. The results show that the intonation of the topic in English information structures is mainly implemented as a pitch accent, except for the type of the pronoun topic (Lp) which is not implemented as a pitch accent or a tune. However, the intonation of the focus is implemented as a tune in the neutral focus (Fn) and as a pitch accent or a tune in the informational focus (Fi) and the contrastive focus (Fe). In our discussion and conclusion, we suggest that it is not always true that for the meaning of contrast, the topic or the focus is represented as a $L+H^{\ast}$ pitch accent, which has been the main contrastive intonation from earlier studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1865-1872
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• 2009

This study suggests a method for extending XMDR to integrate and search legacy system. This extension blends MSO(Meta Semantic Ontology) for the management of metadata, ML(Meta Location) for the management of location information, and Topic Map which is the standard language used to represent semantic web. This study refers to it as TMDR(Topic Map MetaData Registry). As an intelligent layer, Topic Map functions like an index. However, if the data frequently changes, the efficiency of Topic Map may drop. To solve this problem, the proposed system represents the relation among metadata, the relation among real data, and the relation between metadata and real data as Topic Map. The represented Topic Map proposes a method to reduce the changing relation among real data caused by the relation among metadata.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.970-973
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• 2012

In cloud environment, enterprises use a number of systems which are not interconnected and save master information in a distributed way in each of them. Master information which is not managed is incorrect and discord each other so that lowers efficiency of business process and disables optimum decision making. It is necessary to do high-qualified management of master information to operate efficient and errorless business process. This paper suggests TMDM as a solution to solve heterogeneous problems occurred between interconnected master information in cloud environment and to manage business process in an efficient method. TMDM is an information storage that is suggested to solve mutual discord problems between master information using Topic Maps that considers correlation between data. Topic Maps can be connected by association between topics in order to access through a topic to all related knowledge information which is described by the topic. This also can be applied to master information between legacy systems within cloud.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.