• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of the Korean Chemical Society
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• v.66 no.5
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• pp.376-389
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• 2022

The purpose of this study was to investigate the effects of implementing semantic mapping reading strategy in the science class on high school students' science text reading ability. 3^rd grade students of science core high school in a small and medium-sized city participated in this study for a semester. Texts with socio-scientific issues and chemistry subjects were used to implement semantic mapping reading strategy in the science class. To investigate the changes in students' science text reading ability, experimental group students participated in the pre-reading and post-science reading ability tests and the results were analyzed. The results of this study showed that the mean of the science reading ability test score of experimental group was significantly higher than that of the comparison group. We found that drawing a semantic mapping before solving a reading task made it easier for students to find information and infer meaning from text. It can be seen that students also recognize that the semantic mapping is helpful in understanding the text because it is easy to understand the relationship between concepts by visualizing the content of the text, and can connect their background knowledge with the text content.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.395-410
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• 2024

Text data is usually made up of a wide variety of unique words. Even in standard text data, it is common to find tens of thousands of different words. In text data analysis, usually, each unique word is treated as a variable. Thus, text data can be regarded as a dataset with a large number of variables. On the other hand, in text data classification, we often encounter class label imbalance problems. In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded. To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used. The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm. However, in datasets with a large number of variables, such as text data, errors may accumulate. This can potentially impact the performance of the kNN algorithm. In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data. Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.216-219
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• 2003

본 논문은 최소한의 형태소 정보를 이용한 자동 발음열 생성 시스템을 제안한다 일반적으로 발음열 생성 시스템은 입력된 문장에 대하여 형태소 단위로 분석한 다음, 각 형태소와 형태소의 결함 관계를 고려한 음운 규칙을 적용함으로써 상응하는 발음열을 생성한다. 지금까지의 연구는 이러한 발음열 생성시의 형태소 분석에 관하여 그 범위에 관한 연구 없이, 가능한 최대한의 분석을 상정하고 있다. 본 논문은 한국어 음운현상을 체계적인 텍스트 분석을 통하여 모든 형태론적 음운론적인 환경에서 가능한 모든 음운현상을 분류하여 발음열 생성시에 실제로 필요한 형태소 분석의 범위를 규명하는 것을 그 목적으로 한다. 음운 현상을 분석하기 위해 사용한 텍스트 자료로는 어휘가 중복되지 않으면서도 많은 종류의 어휘가 수록된 5만 여 어휘의 연세한국어사전과 2200 여 개의 어미와 조사를 수록한 어미조사사전을 이용하였다. 이와 같이 텍스트를 분석한 결과, 음운현상은 규칙적인 음운 현상과 불규칙적인 음운현상으로 나뉘는데, 이 가운데 형태소 정보가 필요한 형태음운규칙으로는 두 가지가 있으며, 이러한 형태음운규칙을 위한 형태소 분석의 범위로는 세세한 분류를 필요로 하지 않는 최소한의 정보로 가능함을 보인다. 이러한 체계적인 분석을 기반으로 제안하는 자동 발음열 생성 시스템은 형태음운규칙과 예외규칙, 그리고 일반음운 규칙으로 구성된다. 본 시스템에 대한 성능 실험은 PBS 1637 어절과 ETRI 텍스트 DB 19만 여 어절을 이용하여 99.9%의 성능결과를 얻었다.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.715-720
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• 2020

In Big data analysis, text data is mostly unstructured and large-capacity, so analysis was difficult because analysis techniques were not established. Therefore, this study was conducted for the possibility of commercialization through verification of usefulness and problems when applying the big data word cloud technique, one of the text data analysis techniques. In this paper, the limitations and problems of this technique are derived through visualization analysis of the "President UN Speech" using the R program word cloud technique. In addition, by proposing an improved model to solve this problem, an efficient method for practical application of the word cloud technique is proposed.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.249-255
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• 2021

This study proposed a method for determining weights for the eight quality characteristics, such as functionality, reliability, usability, maintainability, portability, efficiency, security, and interoperability, which are suggested by international standards, focusing on software test reports. Currently, the test results for software quality evaluation apply the same weight to 8 quality characteristics to obtain the arithmetic average. Weights for 8 quality characteristics were applied using the results from text analysis, and weights were applied using the results of text analysis of test reports for two products. It was confirmed that the average of test reports according to the weighted quality characteristics was more efficient.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.147-159
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• 2022

Nowadays, various social media platforms are widely spread and people closely use such platforms in daily life. By doing so, social influencers with a large number of subscribers, views, and comments have huge impact in our society. Following this trend, many companies are actively using influencers for marketing purpose to promote their products and services. In this study, we extract the speeches of influencers from videos for game marketing and analyze them using various text mining techniques. In the analysis, we distinguish game videos leading to successful marketing and failed marketing, and we explore and compare the linguistic features of the influencers for successful and failed marketings.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.300-304
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• 2022

본 논문은 한국어 법률 텍스트 처리를 위해 세 가지 서로 다른 사전 학습 모델을 미세 조정하여 그 성능을 평가하였다. 성능을 평가하기 위해 타겟 판결 요지에 대한 판결 요지 후보를 추출하여 판결 요지 간의 유사도를 계산하였다. 또한 유사도를 바탕으로 추출된 판결 요지가 실제 법률 전문가와 일반 언어학자의 직관에 부합하는지 판단하기 위해 정성적 평가를 진행하였다. 그 결과 법률 전문가가 법률 전문 지식이 없는 일반 언어학자에 비해 판결 요지 간 유사도를 낮게 평가하였는데 법률 전문가가 법률 텍스트의 유사성을 판단하는 기준이 기계와 일반 언어학자와는 달라 전문가 자문에 기반한 한국어 법률 AI 모델 개발의 필요성을 확인하였다. 최종 연구 결과로 한국어 법률 AI 프레임워크를 제안하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.395-398
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• 2021

최근 방대한 양의 텍스트 정보가 인터넷에 유통되면서 정보의 핵심 내용을 파악하기가 더욱 어려워졌으며, 이로 인해 자동으로 텍스트를 요약하려는 연구가 활발하게 이루어지고 있다. 텍스트 자동 요약을 위한 다양한 기법 중 특히 트랜스포머(Transformer) 기반의 모델은 추상 요약(Abstractive Summarization) 과제에서 매우 우수한 성능을 보이며, 해당 분야의 SOTA(State of the Art)를 달성하고 있다. 하지만 트랜스포머 모델은 매우 많은 수의 매개변수들(Parameters)로 구성되어 있어서, 충분한 양의 데이터가 확보되지 않으면 이들 매개변수에 대한 충분한 학습이 이루어지지 않아서 양질의 요약문을 생성하기 어렵다는 한계를 갖는다. 이러한 한계를 극복하기 위해 본 연구는 소량의 데이터가 주어진 환경에서도 양질의 요약문을 생성할 수 있는 문서 요약 방법론을 제안한다. 구체적으로 제안 방법론은 한국어 사전학습 언어 모델인 KoBERT의 임베딩 행렬을 트랜스포머 모델에 적용하는 방식으로 문서 요약을 수행하며, 제안 방법론의 우수성은 Dacon 한국어 문서 생성 요약 데이터셋에 대한 실험을 통해 ROUGE 지표를 기준으로 평가하였다.

• Annual Conference of KIPS
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• 2008.05a
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• pp.1104-1107
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• 2008

These people where generally the image or the document nik technique silver document image, against the digital data of audio back all type the research is advanced being used with objective and the use which are various, is a d. Needs a low-end leveling instrument security text from the research which it sees and with substitution quantity the silver nik being simple it will be able to deliver the technique which is simple it embodied. It combined the text image first and the nose which is in the collar image of 24 bit depth which will reach ting it did and it rehabilitatedded and a higher officer technique and the result it used that the loss ratio of the text image to analyze is slight it was ascertained.