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워드임베딩과 그래프 기반 준지도학습을 통한 한국어 어휘 감성 점수 산출
서덕성(Deokseong Seo),모경현(Kyoung Hyun Mo),박재선(Jaesun Park),이기창(Gichang Lee),강필성(Pilsung Kang) 대한산업공학회 2017 대한산업공학회지 Vol.43 No.5
Sentiment analysis plays an important role in both public and private sectors to understand consumers’ responses to products or voters’ reactions to policies. One of the most key success factors of sentiment analysis is to build an appropriate sentiment word dictionary. Many current existing approaches either heavily rely on the knowledge of domain experts or word co-occurrence statistics, the first of which causes low efficiency and high expenditure while the second of which suffers from incomplete data. In order to resolve these shortcomings, we propose a new domain-specific Korean word sentiment score evaluation method based on word embedding and graph based semi-supervised learning. First, words are embedded in a lower dimensional space by Word2Vec technique. Then, the word relation graph is constructed based on the similarity between words in the embedding space. Then, we assign sentiments to approximately 1% words utilizing some indicators like centrality measure. The sentiment scores of the other unlabeled words are automatically assigned by label propagation with semisupervised learning. To verify our proposed method, we collect 1.98 million review comments from three movie review websites. Experimental results show that the proposed method achieves about 93% accuracy of polarity classification.
인스타그램 기반의 전이학습과 게시글 메타 정보를 활용한 페이스북 스팸 게시글 판별
김준홍(Junhong Kim),서덕성(Deokseong Seo),김해동(Haedong Kim),강필성(Pilsung Kang) 대한산업공학회 2017 대한산업공학회지 Vol.43 No.3
This study develops a text spam filtering system for Facebook based on two variable categories: keywords learned from Instagram and meta-information of Facebook posts. Since there is no explicit labels for spam/ham posts, we utilize hash tags in Instagram to train classification models. In addition, the filtering accuracy is enhanced by considering meta-information of Facebook posts. To verify the proposed filtering system, we conduct an empirical experiment based on a total of 1,795,067 and 761,861 Facebook and Instagram documents, respectively. Employing random forest as a base classification algorithm, experimental result shows that the proposed filtering system yield 99% and 98% in terms of filtering accuracy and F1-measure, respectively. We expect that the proposed filtering scheme can be applied other web services suffering from massive spam posts but no explicit spam labels are available.
Development of intelligent safety monitoring model for industrial construction site application
Joo-Hye Park(박주혜),Changgeol Yoon(윤창걸),Seyeong Jeong(정세영),Deokseong Seo(서덕성),Insik Jo(조인식),Youngjun Kim(김영준) 한국경영과학회 2022 한국경영과학회 학술대회논문집 Vol.2022 No.6
Industrial sites and construction sites require a lot of attention to safety accidents. When an accident occurs in this field, the need and importance of a safety monitoring system is getting more attention because it is necessary to quickly recognize an accident and take an appropriate response. In this paper, we propose an image detection model that simultaneously performs person(normal)/head detection, fire/smoke detection, and fallen person detection.