• Journal of Digital Convergence
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• v.11 no.6
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• pp.151-157
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• 2013

This research is about set-up own posture standard and enables to compare the set-up and own posture; therefore, this posture training system leads the user's active learning through the method of self-directed learning. This system includes the camera part that shoots user's posture and save the movie clip for comparison about the user and target, the training server that provides a comparison screen playing clips simultaneously by user's input, and the terminal equipment that connects the training server and a network, transmits the user's input to the server, and displays them from the comparison screen. The Journal of Digital Policy & Management. This space is for the abstract of your study in English.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• Journal of the Korean Institute of Educational Facilities
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• v.5 no.2
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• pp.16-29
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• 1998

Bell Laboratory introduced the sound and visual system for the first time in 1927. Since then, the development of telecommunication technology made it possible for people located far away to communicate each other watching through the TV screen. Over the period different types of telelecturing systems have prospered in line with the development of telecommunication technology. Therefore, it is quite natural that telelecturing/conferending system attracts the attention of many people as a new way of educating people located in a long distance. In the industrial sector, telelecturing systems already come into wide use to save time and training and travelling expense. In this study, I examine the concept and characteristics of telelecturing/conferencing system and introduce different types telelecturing system developed in parallel with the development of communication technology. Then, I analyze how those merits of the telelecturing system can be applied to educational purpose. Finally, I propose and design ideal telelectuirng/conference facilities consisting of telelecturing rooms, bilateral movie system, seats, ceilings, color, TV screen, lighting, acoustics, humidities and temperature control, security system, projection system to maximize the educational purpose and effectiveness.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Journal of the Korean Society of Food Culture
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• v.20 no.5
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• pp.615-620
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• 2005

This study was conducted to develop and evaluate food safety training program for employees in foodservice operations. The developed CD-ROM was consisted of 5 parts: foodbome illness, personal hygiene, food handling in food production steps, HACCP system, and sanitary facilities. It has made slides for all contents, the animation and movie to raise interests and concentrations, and illustrations and pictures to understand. The evaluation checklists were developed 15 questionnaires including understanding(5), information(3), concentration(4), recommendation(2), and the most important factor(1) and measured by Likert 5-point scale. Fifty-four dietitians in Daegu and Gyeongbuk schools, hospitals, and industries foodservice operations responded to the surveys. The results are as follows; The most important part in the CD-ROM was personal hygiene(33.3%). The total mean was $3.95{\pm}0.41,\;3.91{\pm}0.46$ on understanding, $3.89{\pm}0.50$ on information, $3.87{\pm}0.55$ on concentration and $4.29{\pm}0.49$ on recommendation. The score was significantly higher in the recommendation part than others. Overall, as the developed CD-ROM has achieved fine score, a study on the effect of education needs to be followed. Moreover, consistent and organized education by developing a variety of sanitation education methods should be conducted.

• Journal of Korea Entertainment Industry Association
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• v.13 no.7
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• pp.135-143
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• 2019

This study analyzes the present condition and the environment of the university education centered on the practice in the film field and suggests the effective development plan for the new or improved practice. Recently, as a new breakthrough in infant education in the rapidly declining educational environment, the introduction of arts education, on-site self-study, and practice-centered laboratory exercises are increasing students' satisfaction. However, compared to trend, our practical education and creative education programs are lacking in reality. In this study, we analyze and study the effective development method of education through researching the satisfaction pattern and the education program of the students who are the center of the production practice shown in the movie image production, and building the paradigm, I would like to suggest a direction for the production education.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.15-30
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• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.