• Language and Information
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• v.14 no.2
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• pp.131-157
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• 2010

This article reports an attempt to perform a similarity analysis on a collection of 25 culinary manuscripts in Chosun periods using a set of quantitative text analysis methods. Historical culinary texts are valuable resources for linguistic, historic, and cultural studies. We consider the similarity of two texts as the distributional similarities of the functional components of the texts. In the case of culinary texts, text elements such as food names, cooking methods, and ingredients are regarded as functional components. We derive the similarity information from the distributional characteristics of the two key functional components, cooking methods and ingredients. The results are also quantified and visualized to achieve a better understanding of the properties of the individual texts and the collection of the texts as a whole.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.83-92
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• 2013

Text visualization begins with understanding text itself which is material of visual expression. To visualize any text data, sufficient understanding about characteristics of the text first and the expressive approaches can be decided depending on the derived unique characteristics of the text. In this research we aimed to establish theoretical foundation about the approaches for text visualization by diverse examples of text visualization which are derived through the various characteristics of the text. To do this, we chose the 'Bible' text which is well known globally and digital data of it can be accessed easily and thus diverse text visualization examples exist and analyzed the examples of the bible text visualization. We derived the unique characteristics of text-content, structure, quotation- as criteria for analyzing and supported validity of analysis by adopting at least 2-3 examples for each criterion. In the result, we can comprehend that the goals and expressive approaches are decided depending on the unique characteristics of the Bible text. We expect to build theoretical method for choosing the materials and approaches by analyzing more diverse examples with various point of views on the basis of this research.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.68-74
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• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

• International Journal of Contents
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• v.14 no.1
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• pp.1-6
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• 2018

Text, as one of the most influential inventions of humanity, has played an important role in human life since ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications such as the text data extracted from images that can provide information for automatic annotation, indexing, language translation, and the assistance systems for impaired persons. Therefore, natural-scene text detection with active research topics regarding computer vision and document analysis is very important. Previous methods have poor performances due to numerous false-positive and true-negative regions. In this paper, a fully-convolutional-network (FCN)-based method that uses supervised architecture is used to localize textual regions. The model was trained directly using images wherein pixel values were used as inputs and binary ground truth was used as label. The method was evaluated using ICDAR-2013 dataset and proved to be comparable to other feature-based methods. It could expedite research on text detection using deep-learning based approach in the future.

• International Journal of Contents
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• v.11 no.4
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• pp.77-85
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• 2015

Table detection is a challenging problem and plays an important role in document layout analysis. In this paper, we propose an effective method to identify the table region from document images. First, the regions of interest (ROIs) are recognized as the table candidates. In each ROI, we locate text components and extract text blocks. After that, we check all text blocks to determine if they are arranged horizontally or vertically and compare the height of each text block with the average height. If the text blocks satisfy a series of rules, the ROI is regarded as a table. Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging. This proves the effectiveness and superiority of our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2186-2196
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• 2014

Spam mail is one of the most general mail dysfunctions, which may cause psychological damage to internet users. As internet usage increases, the amount of spam mail has also gradually increased. Indiscriminate sending, in particular, occurs when spam mail is sent using smart phones or tablets connected to wireless networks. Spam mail consists of approximately 68% of mail traffic; however, it is believed that the true percentage of spam mail is at a much more severe level. In order to analyze and detect spam mail, we introduce a technique based on spam mail characteristics and text mining; in particular, spam mail is detected by extracting the linguistic analysis and language processing. Existing spam mail is analyzed, and hidden spam signatures are extracted using text clustering. Our proposed method utilizes a text mining system to improve the detection and error detection rates for existing spam mail and to respond to new spam mail types.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.295-309
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• 2020

In 2019, Japan announced trade regulations against Korean products; consequently, the sales of Japanese products in Korea dropped due to a Korean consumers' boycott. This study measured the Korean consumers' political consumption behavior toward Japanese fashion products. Unstructured text data from online media sources and consumer posted sources such as blog and SNS were collected. Text mining techniques and semantic network analysis were used to process unstructured data. This study used text mining techniques and semantic network analysis to process data. The results identified boycotting Japanese fashion products and buycotting alternative products and Korean brands due to consumers' political consumption. Two brand cases were investigated in detail. Online text data before and after the political action were compared and significant changes in consumption as well as emotional expressions were identified. Product related industry sectors were identified in terms of the political consumption of fashion: liquor, automobile and tourism industry sectors were closely linked to the fashion sector in terms of boycotting. More "boycott" and "buycott" fashion brands (reflected in consumer attitudes and feelings) were detected in consumer driven texts than in media driven sources.

• Lingua Humanitatis
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• v.5
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• pp.127-163
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• 2003

This thesis analyzes the Chapter "Hoyeonjigi(浩然之氣)" of Mencius(孟子), using text linguistics theory and reading theory of Korean. In this process the model of macro-structure #1∼5 are presented, according to Vandijk′s rules of macro-structure; Auslassen, Selektierne, Generalisieren, Konstruieren odor Integrieren. As a result, this certifies; First, macro-structure could make arbitrarily a several steps of macro-structure by types of text or purpose of analysis. Second, macro-structure applies various cognitive mechanisms of outer world as well as inner world. Third, a text with profound symbolism could be figured as a two-or threefolded symbolic structure. At the same time, macro-structure enables the clearer analysis of the content of the Chapter to verify the following; first, Hoyeinjigi itself is the best measure of developing "Imperturbable Mind(不動心)" Second, benevolence-righteousness(仁義) and wisdom(智) would be reached by cultivating Hoyeonjigi. Third, Mencius′ own view of language is well expressed in "Jieon(知言)", which is not only a condition for Imperturbable Mind, but also the Oriental view of language focused especially on listening in terms of language usage, not language analysis. This Mencius′ view of language has a thread connection with that of Oriental′s.