• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1751-1754
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• 2022

The news articles collected with keyword "non face-to-face" were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-face-to-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.59-66
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• 2008

This paper describes a novel method for identifying the main topic and detecting topic changes in a text-based dialogue as in Instant Messaging (IM). Compared to other forms of text, dialogues are uniquely characterized with the short length of text with small number of words, two or more participants, and existence of a history that affects the current utterance. Noting the characteristics, our method detects the main topic of a dialogue by considering the keywords not only the utterances of the user but also the dialogue system's responses. Dialogue histories are also considered in the detection process to increase accuracy. For topic change detection, the similarity between the former utterance's topic and the current utterance's topic is calculated. If the similarity is smaller than a certain threshold, our system judges that the topic has been changed from the current utterance. We obtained 88.2% and 87.4% accuracy in topic detection and topic change detection, respectively.

• Language and Information
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• v.9 no.2
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• pp.85-105
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• 2005

This study aims at investigating the 'verb-second' phenomena indicated in complex sentences of medieval Spanish. Especially, when the complex sentence is composed of a preposed adverbial clause and its succeeding main clause, the subject inversion is noticeable in the latter. The fundamental motive of this type of inversion is due to the 'verb-second' structure, in which a topic appears in the first position and the verb immediately after the topic. So it can be said that the subject inversion is a prerequisite for a verb to be located in the second position when the adverbial clause functions as a topic to the main clause, as is often the case with Germanic languages like German, Dutch, etc.. On the contrary, modern Spanish complex sentences do not show this phenomenon, with a strong tendency to locate a grammatical subject in the preverbal position. Therefore, medieval Spanish might be typologically closer to Germanic languages than to modern Spanish. In order to argue for this assumption, the formal and functional criteria by which the preposed adverbial clause could be defined as a topic NP will be examined across the comparition with left-dislocation structure.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.855-870
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• 2012

The paper deals with three main topics: a) the definition of System Thermal-Hydraulics (SYS TH), b) a historical outline for SYS TH and, c) the description of elements for reflection when planning research projects or improvement activities, this last topic being the main reason for the paper. Distinctions between basic thermal-hydraulics and computational Fluid-Dynamics (CFD) on the one side and SYS TH on the other side are considered under the first topic; stakeholders in the technology are identified. The proposal of Interim Acceptance Criteria for Emergency Core Cooling Systems in 1971 by US NRC (AEC at the time) is recognized as the starting date or the triggering event for SYS TH (second topic). The complex codes and the main experimental programs (list provided in the paper) constitute the pillars for SYS TH. Caution or warning statements are introduced in advance when discussing the third topic: a single person (or a researcher) has little to no possibility, or capability, of streamlining the forthcoming investments or to propose a roadmap for future activities. Nevertheless, the ambitious attempt to foresee developments in this area has been pursued without constraints connected with the availability of funds and with industrial benefits or interests. Demonstrating the acceptability of current SYS TH limitations and training in the application of those codes are mentioned as the main challenges for forthcoming research activities.

• The Journal of Information Systems
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• v.30 no.1
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• pp.45-64
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• 2021

Purpose The traditional topic modeling technique makes it difficult to distinguish the semantic of topics because the key words assigned to each topic would be also assigned to other topics. This problem could become severe when the number of online reviews are small. In this paper, the extended model of topic modeling technique that can be used for analyzing a small amount of online reviews is proposed. Design/methodology/approach The extended model of being proposed in this paper is a form that combines the traditional topic modeling technique and the Word2Vec technique. The extended model only allocates main words to the extracted topics, but also generates discriminatory words between topics. In particular, Word2vec technique is applied in the process of extracting related words semantically for each discriminatory word. In the extended model, main words and discriminatory words with similar words semantically are used in the process of semantic classification and naming of extracted topics, so that the semantic classification and naming of topics can be more clearly performed. For case study, online reviews related with Udo in Tripadvisor web site were analyzed by applying the traditional topic modeling and the proposed extension model. In the process of semantic classification and naming of the extracted topics, the traditional topic modeling technique and the extended model were compared. Findings Since the extended model is a concept that utilizes additional information in the existing topic modeling information, it can be confirmed that it is more effective than the existing topic modeling in semantic division between topics and the process of assigning topic names.

• 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 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.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.39-53
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• 2018

While the body of research on metadata has grown substantially, there has been a lack of systematic analysis of the field of metadata. In this study, we attempt to fill this gap by examining metadata literature spanning the past 20 years. With the combination of a text mining technique, topic modeling, and network analysis, we analyzed 2,713 scholarly papers on metadata published between 1995 and 2014 and identified main topics and trends in metadata research. As the result of topic modeling, 20 topics were discovered and, among those, the most prominent topics were reviewed in detail. In addition, the changes over time in the topic composition, in terms of both the relative topic proportions and the structure of topic networks, were traced to find past and emerging trends in research. The results show that a number of core themes in metadata research have been established over the past decades and the field has advanced, embracing and responding to the dynamic changes in information environments as well as new developments in the professional field.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.