• Korean journal of communication and information
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• v.16
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• pp.36-70
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• 2001

The purpose of this study is to investigate women's radio talk program and evaluate its possibilities and limitation from the point of feminist perspective. The theoretical framework is based on feminist studies and text analysis of talk show. Existing studies regarding talk show are mainly focused on television. But radio talk show is one of general entertainment and it's influences on minorities are still significant. is the most representative women's radio talk program in Korea. It has been broadcasted over 10 years and very popular among Korean housewives. The audience of this program call themselves schoolfellow, and call the program 'school of women'. The media text of is mainly consisted of letters from women audiences, and they are selected by producers. So the text is made by both audiences and producers. The unique combination of this process produces complicated discourses which contain women's experiences in letters and station's considerations through safekeeping. The problems investigated in this study are as follows: First, What discourses are produced in this program? Second, Alternative possibilities can be seen in this program in feminist perspectives? Text analysis of 1week(2000.9.18-9.24) and interview with producers are accomplished to this purposes. In the text analysis, subject matters, inscribed women's position, values of the letters are revealed. Most of the subject matters are family affairs. Some are socially oriented but family and home are the predominant category of women's letters. And the position of women subject is defined in the domestic network. They are nameless but the mother, wife, daughter, daughter-in-law of other people. In value, family-oriented value and small happiness in everyday life are generally appeared. But these values are essentially coincide with the values of status quo. The answers of the conflict are not public but individualized. And acception the status quo is presented as the wisest decision, But ` has many implications in relation to women's sharing of their experience, and construction of imagined community in media. Women continuously interact each other revealing and discussing their experiences and sometimes their social practices are stirred through this media sphere. So we see the 'emotional union' among women are formed through radio. The limitation of this program is very apparent: it's patriarchic values, acception of status quo, and individualization of the women's problems. But in the same time we can read coexisting it's latent possibilities: the possibilities of women's public sphere. But it is completely alternative women's sphere in feminist perspectives. It renders women opportunities to participate public media and share with other women, and collaborate with their problem.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Journal of the Ergonomics Society of Korea
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• v.30 no.2
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• pp.303-310
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• 2011

In this study, we employ CPM-GOMS analysis for explaining physical and cognitive processes and for quantitatively predicting when users are typing Korean text messages on mobile phones using both hands. First, we observe the behaviors of 10 subjects, when the subjects enter keypads with both hands. Then, basing upon MHP, we categorize the behaviors into perceptual, cognitive, motor operators, and then we analyze those operators. After that, we use the critical paths to model two task sentences. Also, we used Fitts' law method which was applied many times to predict text entering time on mobile phone to compare with the results of our CPM-GOMS model. We followed Lee's (2008) method that is well suited for text entry task using both hands and calculate total task time for each task sentences. For the sake of comparison between the actual data and the results predicted from our CPM-GOMS model, we empirically tested 10 subjects and concluded that there were no significant differences between the predicted values and the actual data. With the CPM-GOMS model, we can observe the human information processes composed on the physical and cognitive processes. Also we verified that the CPM-GOMS model can be well applied to predict the users' performance when they input text messages on mobile phones using both hands by comparing the predicted total task time with the real execution time.

• Phonetics and Speech Sciences
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• v.10 no.1
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• pp.39-48
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• 2018

A typical statistical parametric speech synthesis (text-to-speech, TTS) system consists of separate modules, such as a text analysis module, an acoustic modeling module, and a speech synthesis module. This causes two problems: 1) expert knowledge of each module is required, and 2) errors generated in each module accumulate passing through each module. An end-to-end TTS system could avoid such problems by synthesizing voice signals directly from an input string. In this study, we implemented an end-to-end Korean TTS system using Google's Tacotron, which is an end-to-end TTS system based on a sequence-to-sequence model with attention mechanism. We used 4392 utterances spoken by a Korean female speaker, an amount that corresponds to 37% of the dataset Google used for training Tacotron. Our system obtained mean opinion score (MOS) 2.98 and degradation mean opinion score (DMOS) 3.25. We will discuss the factors which affected training of the system. Experiments demonstrate that the post-processing network needs to be designed considering output language and input characters and that according to the amount of training data, the maximum value of n for n-grams modeled by the encoder should be small enough.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.111-129
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• 2009

This study examined the usability of text-based search and visual search of a large multidisciplinary library database to provide an empirical analysis of the acceptability of visual systems in the information retrieval environment. It also examined if there are differences in the usability assessment based on experimental order. The results indicated that the text-based search supported users' search behaviors more efficiently than the visual search. Also the text-based search was rated higher than the visual search in terms of user perceptions of four usability factors.

• Fashion & Textile Research Journal
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• v.11 no.2
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• pp.210-218
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• 2009

The Purpose of this study is to demonstrate Storytelling as an effective device for Fashion Design by establishing relationship between Fashion Design Process and Storytelling. Through researching a social background and a concept of storytelling, found that story used interactively is a powerful tool for attention, understanding and change in both individuals and communities. Analysed the elements and the structure of storytelling and Fashion Design Process, by researching preceding researches. Therefore, we proposed a new four elements -text factor, visual factor, audio factor and virtual factor- and four steps (1)exploring stories, (2)planning a story, (3)building the story, (4)do storytelling- of storytelling and four steps-(1)gathering and analysing informations, (2)building a concept, (3)planning and developing a design, (4)do evaluation and make decision- of fashion design process. Through comparative analysis, we found a closeness between two structures, a use of common factors and also found characteristics to be considered in each stage. In the first stage, we found text, visual and audio factor as common factors. In the second stage, we suggested text and visual factor as common factors and also suggested clarity, realism and probability as characteristics. In the third stage, we found text, visual and virtual factor and also found dynamism, immersion and continuity. In the last stage, we suggested text, visual, virtual and audio factor and also suggested presence and interactivity as characteristics.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.22-26
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• 2005

In this paper, we classified the objectionable texts into four rates according to their harmfulness and proposed the hierarchical text rating system for objectionable documents. Since the documents in the same category have similarities in used words, expressions and structure of the document, the text rating system, which uses a single classification model, has low accuracy. To solve this problem, we separate objectionable documents into several subsets by using their properties, and then classify the subsets hierarchically. The proposed system consists of three layers. In each layer, we select features using the chi-square statistics, and then the weight of the features, which is calculated by using the TF-IDF weighting scheme, is used as an input of the non-linear SVM classifier. By means of a hierarchical scheme using the different features and the different number of features in each layer, we can characterize the objectionability of documents more effectively and expect to improve the performance of the rating system. We compared the performance of the proposed system and performance of several text rating systems and experimental results show that the proposed system can archive an excellent classification performance.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.149-156
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• 2021

Text mining is one of the useful tools to discover public opinion and perception regarding political issues from big data. It is very common that users of social media express their opinion with newly-coined words such as slang and emoji. However, those new words are not effectively captured by traditional text mining methods that process text data using a language dictionary. In this study, we propose effective methods to extract newly-coined words that connote the political stance and opinion of users. With various text mining techniques, I attempt to discover the context and the political meaning of the new words.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.269-271
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• 2022

In recent years, RNN networks with LSTM blocks have been used extensively in machine learning tasks that process sequential data. These networks have proven to be particularly good at sequential language processing tasks by being more able to accurately predict the next most likely word in a given sequence than traditional neural networks. This study trained an RNN / LSTM neural network on three different translations of 150 biblical Psalms - in both English and Korean. The resulting model is then fed an input word and a length number from which it automatically generates a new Psalm of the desired length based on the patterns it recognized while training. The results of training the network on both English text and Korean text are compared and discussed.