• Archives of design research
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• v.19 no.1 s.63
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• pp.129-140
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• 2006

In this study, I would like to suggest a new model of web navigation design which can enhance old-aged users web usability. I would like to prove that when users enjoy web navigation design, older-users access information much more effectively. Using a survey, suggest some types of web navigation designed for old-aged users. After that, based on the result of the prepatory survey, I conducted an experiment with 4 types of web navigation designs, which have been developed to reflect varying stages of comprehensibility. My survey focused three points. First I tested whether they lost their way while searching for information. Secondly, if they did lose their way, I checked whether they could recover from their errors and find their way back. Thirdly, I investigated whether layout, location and size of the web navigation design factors affected usability. The results of my survey indicated that old-aged users spend the shortest time, have the easiest interface, and have least error incidence under the web navigation design to enhance old-aged users web usability.Thus, developing a web navigation design for old-aged users can encourage older people to be more involved with the internet, especially in the aging society.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.1-7
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• 2019

With the in-depth development of machine learning, the deep learning method has made great progress, especially with the Convolution Neural Network(CNN). Compared with traditional text sentiment classification methods, deep learning based CNNs have made great progress in text classification and processing of complex multi-label and multi-classification experiments. However, there are also problems with the neural network for text sentiment classification. In this paper, we propose a fusion model based on Long-Short Term Memory networks(LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• Archives of design research
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• v.19 no.4 s.66
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• pp.101-110
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• 2006

In this study, I suggested a new model of universal web navigation design which can enhance users' web usability. I would like to prove that when users enjoy web navigation design, they access information much more effectively. Using a survey, I suggest some types of web navigation design for users. After that, I conducted an experiment with 4 types of web navigation designs, which have been developed to reflect varying stages of comprehensibility. My survey focused on three points. First I measured the time they spent to search for information. Second, I tested whether they lost their way while searching for information and if they did lose their way, I checked whether they could recover from their errors and find their way back. Third, I investigated whether layout, location and size of the web navigation design factors affected usability. The results of my survey indicated that users spend the shortest time, have the easiest interface, and have least error incidence under the web navigation design to enhance old-aged users' web usability. Thus, developing a universal web navigation design can encourage people to be more involved with the internet.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.1-15
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• 2022

In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.