• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.26-29
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• 2016

웹 문서 추천 시스템에서는 유사한 내용의 문서임에도 불구하고 URL이 달라서 다른 문서로 인식하여 사용자에게 추천하는 데이터 희소성 문제가 있다. 여기서 기존 연구들은 이 문제에 대한 해결 방법으로 TF-IDF를 이용하였으나 비용 및 시간의 한계가 있으며 유의어 분류 문제가 있다. 본 논문에서는 Word2Vec을 이용한 웹문서 학습 시스템을 통해 문제를 해결한다. 제안 시스템은 언론사의 뉴스를 수집하고 이를 정형화된 형식으로 분석하여 가공하는 전처리 과정을 거친 후 Word2Vec 학습을 통해 문서 벡터를 생성하고 이를 K-Means 클러스터링으로 유사 문서군으로 분류한다. 이 시스템을 이용하면 데이터 희소성 문제를 해결할 뿐만 아니라 연산량이 TF-IDF에 비해 줄어들고 유의어 분류 시 유사도가 높아지는 강점이 있다.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.183-188
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• 2021

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.79-80
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• 2021

인터넷은 우리 경제를 디지털 경제로 변화시키며 전자상거래도 증가하고 있다. 따라서 구매자가 전자상거래에서 남기는 긍정적인, 부정적인 상품평은 상품기획의 주요 정보가 될 수 있다. 본 논문에서는 버티컬 무소음 마우스 10,000개에 대한 정형화된 데이터셋을 Word2Vec을 이용하여 유사도 분석, 온라인 상품평 빈도분석 상위 50개 단어를 제시하여 실제 상품을 사용한 후 설문조사 시행을 하였다. 온라인 상품평 유사도 분석결과 클릭 키워드에 대한 장점으로 통증(.986), 디자인(.982)가 분석되었으며 단점은 적응(.866), 불편(.854)이었다. 오프라인 상품평에서는 장점으로 디자인(17명), 단점으로 불편(11명)이었다. 또한 온라인과 오프라인의 상품평을 비교함으로써 구매자의 긍정, 부정의 의미를 교차 확인하여 유의미한 정보를 제시 하였다고 볼수 있다. 따라서 본 연구에서 제시하는 상품기획 프로세스를 신상품 개발 및 기존 상품의 개선 전략으로 적용할 수 있겠다.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.833-844
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• 2023

As technology convergence is recognized as a driver of innovation, the identification of technology convergence opportunities is critical to expanding a firm's technology portfolio. Recently, wearable technology has emerged as an important factor in creating new business opportunities and providing technology investment alternatives for firms in the era of Industry 4.0. Against this background, this study provides a new patent analysis framework for identifying and proposing technology convergence opportunities in the wearable field. Using 8,621 patents filed between 2011 and 2021, a case study was conducted to identify technological convergence opportunities by applying Word2Vec algorithm. The analysis framework can be divided into four stages, with the final stage recommending potential technology convergence opportunities for a specific candidate firm's technology area by calculating similarities between technology codes. This study aims to better understand the current status of wearable technology development as well as to propose a new methodology for capturing technology convergence opportunities in the wearable industry. The case study result suggests that the convergence of healthcare and ICT may provide new development opportunities. Furthermore, the results are expected to provide alternative perspectives on the development of new markets and technologies using wearable technology and can support the strategic decision-making on future technology planning in the wearable field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.355-360
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• 2021

Recently Youtube has been more popular. As many studies show the comparative deprivation of the Social Medeia, this study looks into whether the comparative deprivation is expressed on the YouTube comments. It focuses on the Luxury Haul contents, videos about huge amounts of luxurious products, of which Youtubers'economic feature are demonstrative. The comments of the videos are analyzed with LDA TF-IDF and Word2Vec. Additionally, the comments were classified into positive and negative groups by the LSTM model as well. As a result of the study, even though many comments turned out positive, the negative keywords were indicated related to comparative deprivation. Also it was found that the viewers compared themselves with Youtubers. In particular, some YouTubers are more criticized if they are younger or does not seem to afford the luxurious products themselves. This study suggests that the users express the comparative deprivation on YouTube as well like on the other Social Media.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.91-98
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• 2023

Since risks occurring in construction projects can have a significant impact on schedules and costs, there have been many studies on this topic. However, risk analysis is often limited to only certain construction situations,and experience-dependent decision-making is therefore mainly performed. Data-based analyses have only been partially applied to safety and contract documents. Therefore, in this study, cluster analysis and a Word2Vec algorithm were applied to formal documents that contain important elements for contractors or clients. An initial classification of document content into six types was performed through cluster analysis, and 157 occurrence types were subdivided through application of the Word2Vec algorithm. The derived terms were re-classified into five categories and reviewed as to whether the terms could develop into potential construction risk factors. Identifying potential construction risk factors will be helpful as basic data for process management in the construction industry.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.60-63
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• 2017

자연어처리에 딥 러닝을 적용하기 위해 사용되는 Word embedding은 단어를 벡터 공간상에 표현하는 것으로 차원축소 효과와 더불어 유사한 의미의 단어는 유사한 벡터 값을 갖는다는 장점이 있다. 이러한 word embedding은 대용량 코퍼스를 학습해야 좋은 성능을 얻을 수 있기 때문에 기존에 많이 사용되던 word2vec 모델은 대용량 코퍼스 학습을 위해 모델을 단순화 하여 주로 단어의 등장 비율에 중점적으로 맞추어 학습하게 되어 단어의 위치 정보를 이용하지 않는다는 단점이 있다. 본 논문에서는 기존의 word embedding 학습 모델을 단어의 위치정보를 이용하여 학습 할 수 있도록 수정하였다. 실험 결과 단어의 위치정보를 이용하여 word embedding을 학습 하였을 경우 word-analogy의 syntactic 성능이 크게 향상되며 어순이 바뀔 수 있는 한국어에서 특히 큰 효과를 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.60-63
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• 2017

자연어처리에 딥 러닝을 적용하기 위해 사용되는 Word embedding은 단어를 벡터 공간상에 표현하는 것으로 차원축소 효과와 더불어 유사한 의미의 단어는 유사한 벡터 값을 갖는다는 장점이 있다. 이러한 word embedding은 대용량 코퍼스를 학습해야 좋은 성능을 얻을 수 있기 때문에 기존에 많이 사용되던 word2vec 모델은 대용량 코퍼스 학습을 위해 모델을 단순화 하여 주로 단어의 등장 비율에 중점적으로 맞추어 학습하게 되어 단어의 위치 정보를 이용하지 않는다는 단점이 있다. 본 논문에서는 기존의 word embedding 학습 모델을 단어의 위치정보를 이용하여 학습 할 수 있도록 수정하였다. 실험 결과 단어의 위치정보를 이용하여 word embedding을 학습 하였을 경우 word-analogy의 syntactic 성능이 크게 향상되며 어순이 바뀔 수 있는 한국어에서 특히 큰 효과를 보였다.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.36-48
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• 2020

App market has continuously been growth since its launch. The market revenues will reach about 1,000 billion US dollars in 2019. App is a core service for smartphone. Currently, there are more than 1.5 million mobile apps in App platform calling out for attention. So, if you are looking at developing a successful app, you need to have a solid marketing and distribution strategy. Online word of mouth(eWOM) is one of the most effective, powerful App marketing method. eWOM affect potential consumers' decision making, and this effect can spread rapidly through online social network. Despite the increasing research on word of mouth, only few studies have focused on content analysis. Most of studies focused on the causes and acceptance of eWOM and eWOM performance measurement. This study aims to content analysis of mobile apps review In 2013, Google researchers announced Word2Vec. This method has overcome the weakness of previous studies. This is faster and more accurate than traditional methods. This study found out the relationship between mobile app reviews and checked for reactions by Word2vec.