• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.521-523
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• 2021

관광 분야에서 온라인 리뷰의 중요성이 커지고 있다. 온라인 리뷰의 텍스트 데이터는 파악이 어렵다. 이에 본 연구에서는 특정 관광지에 대한 온라인 리뷰 텍스트 데이터가 나타내는 전반적인 의견을 직관적으로 도출하는 방법에 대해 알아보고자, 토픽 모델링과 Markov Chain을 시행했다. '해운대'에 대한 온라인 리뷰를 수집한 후, LDA와 BTM을 활용하여 주제를 도출하고, Markov Chain을 시각화하여 키워드 간의 관계와 전체적인 평가 내용을 확인했다. 사용된 기법은 각자 특징적인 결과를 제시했기 때문에 다양한 기법을 상보적으로 이용하기를 제안하였다.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.477-490
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• 2024

This study identifies the vocabulary usage patterns of Korean heritage language learners. We analyzed the interlanguage of the Korean heritage language learners and examined their vocabulary usage patterns, especially the major content keywords being used at their respective proficiency levels. The Korean Learner's Corpus from the National Institute of Korean Language is used for the data analysis. We found that as the heritage language learners' proficiency increases, low-frequency (high-level) vocabulary is often used as the keywords and the semantic vocabulary areas expand from daily to social to specialized fields. It is therefore confirmed that the vocabulary use of Korean heritage language learners develops as their proficiency increases. This study confirms the development of Korean vocabulary in Korean heritage language learners and exemplifies how corpus-based applied linguistic research and computer science can be integrated using a keyword extraction algorithm.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.71-75
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• 2017

Due to the spread of SNS, web-based consumer-generated data is increasing exponentially. It is important in many fields to accurately extract what is appropriate for the user's interest in a large amount of data. It is especially important for business mangers to establish marketing policies to find the right customers for them in many users. In this paper, we try to obtain important information centering on customers who are interested in each account through Twitter follow - following relationship. Because Twitter's current follower relationships do not reflect the user's interests, we try to figure out the details of interest using keyword extraction methods for tweets of followers. To do this, we select two domestic commercial Twitter accounts and apply the sequential pattern evaluation index to the mining key phrase of the text data collected from the follower.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.248-256
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• 2000

This paper presents a personalized web agent that constructs user profile which consists of user preferences on the web and recommends his/her relevant information to the user. The personalized web agent consists of monitor agent, user profile construction agent, and user profile refinement agent. The monitor agent makes a user describe his/her preferences directly and it creates the database of preference document, finally performs several keyword extraction to increase the accuracy of the DB. The user profile construction agent transforms the extracted keywords into user profile that could be confirmed and edited by the user. and the refinement agent refines user profile by recursively learning and processing user feedback. In this paper, we describe the several keyword weighting and inductive learning techniques in detail. Finally, we describe the adaptive web retrieval and push agent that perform adaptive services to the user.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.231-250
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• 2018

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.317-324
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• 2012

Recently, coordination task of similarity between national R&D programs is emphasized on view from the R&D investment efficiency. But the previous similarity search method like text-based similarity search which using keyword of R&D projects has reached the limit due to deviation of document's quality. For the solve the limitations of text-based similarity search using the keyword extraction, in this study, utilization of R&D project's technical classification will be discussed as a new similarity search method when analyzed of similarity between national R&D programs. To this end, extracts the Science and Technology Standard Classification of R & D projects which are collected when national R&D Survey & analysis, and creates peculiar vector model of each R&D programs. Verify a reliability of this study by calculate the cosine-based and Euclidean distance-based similarity and compare with calculated the text-based similarity.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.115-124
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• 2004

The content-based retrieval using low-level features can hardly provide the retrieval result that corresponds with conceptual demand of user for intelligent retrieval. Video includes not only moving picture data, but also audio or closed-caption data. Knowledge-based video retrieval is able to provide the retrieval result that corresponds with conceptual demand of user because of performing automatic indexing with such a variety data. In this paper, we present the knowledge-based video retrieval system using Korean closed-caption. The closed-caption is indexed by Korean keyword extraction system including the morphological analysis process. As a result, we are able to retrieve the video by using keyword from the indexing database. In the experiment, we have applied the proposed method to news video with closed-caption generated by Korean stenographic system, and have empirically confirmed that the proposed method provides the retrieval result that corresponds with more meaningful conceptual demand of user.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.83-93
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• 2022

Research is underway to solve the traffic problem by using crowdsourcing, where drivers use their mobile devices to provide traffic information. If it is used for traffic event detection through crowdsourcing, the task of collecting related data is reduced, which lowers time cost and increases accuracy. In this paper, we propose a scheme to collect traffic-related data using crowdsourcing and to detect events affecting traffic through this. The proposed scheme uses machine learning algorithms for processing large amounts of data to determine the event type of the collected data. In addition, to find out the location where the event occurs, a keyword indicating the location is extracted from the collected data, and the administrative area of the keyword is returned. In this way, it is possible to resolve a location that is broadly defined in the existing location information or incorrect location information. Various performance evaluations are performed to prove the superiority and feasibility of the proposed scheme.