• Journal of Korean Academy of Nursing
• /
• v.44 no.3
• /
• pp.328-338
• /
• 2014

Purpose: This study was done to explore experiences of persons living through the periods of cancer diagnosis, treatment, and self-care. Methods: With permission, texts of 29 cancer survival narratives (8 men and 21 women, winners in contests sponsored by two institutes), were analyzed using Kang's Korean-Computerized-Text-Analysis-Program where the commonly used Korean-Morphological-Analyzer and the 21st-century-Sejong-Modern-Korean-Corpora representing laymen's Korean-language-use are connected. Experiences were explored based on words included in 100 highly-used-morphemes. For interpretation, we used 'categorizing words by meaning', 'comparing use-rate by periods and to the 21st-century-Sejong-Modern-Korean-Corpora', and highly-used-morphemes that appeared only in a specific period. Results: The most highly-used-word-morpheme was first-person-pronouns followed by, diagnosis treatment-related- words, mind-expression-words, cancer, persons-in-meaningful-interaction, living and eating, information-related-verbs, emotion-expression- words, with 240 to 0.8 times for layman use-rate. 'Diagnosis-process', 'cancer-thought', 'things-to-come-after-diagnosis', 'physician husband', 'result-related-information', 'meaningful-things before diagnosis-period', and 'locus-of-cause' dominated the life of the diagnosis-period. 'Treatment', 'unreliable-body', 'husband people mother physician', 'treatment-related-uncertainty', 'hard-time', and 'waiting-time represented experiences in the treatment-period. Themes of living in the self-care-period were complex and included 'living-as-a-human', 'self-managing-of-diseased-body', 'positive-emotion', and 'connecting past present future'. Conclusion: The results show that the experience of living for persons with cancer is influenced by each period's own situational-characteristics. Experiences of the diagnosis and treatment-period are negative disease-oriented while that of the self-care period is positive present-oriented.

• The Journal of Bigdata
• /
• v.1 no.2
• /
• pp.21-34
• /
• 2016

There are various investment strategy reports available online, prepared by many financial analysts. If the correlation between the title of the report and analyst forecast can be found, we can tell from the title whether analyst' forecast will be reliable or not. The objective of this study is to see the correlation between the title of analyst investment strategy report and the actual result of forecast by using the Text Mining technique. The result of actual analysis showed that "strong buy and sell call" appeared in the title lead the higher accuracy of analyst forecast and fulfillment ratio. The results that potential investors can get better information by reading the title of the analyst report. We hope that this study could be the basis for new methodologies in this area.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2133-2138
• /
• 2017

The analysis of existing XML documents and other documents was centered on words. It can be implemented using a morpheme analyzer, but it can classify many words in the document and cannot grasp the core contents of the document. In order for a user to efficiently understand a document, a paragraph containing a main word must be extracted and presented to the user. The proposed system retrieves keyword in the normalized XML document. Then, the user extracts the paragraphs containing the keyword inputted for searching and displays them to the user. In addition, the frequency and weight of the keyword used in the search are informed to the user, and the order of the extracted paragraphs and the redundancy elimination function are minimized so that the user can understand the document. The proposed system can minimize the time and effort required to understand the document by allowing the user to understand the document without reading the whole document.

• Journal of Convergence for Information Technology
• /
• v.10 no.10
• /
• pp.40-46
• /
• 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 KIISE
• /
• v.43 no.1
• /
• pp.71-79
• /
• 2016

Disambiguation of homographs is an important job in Korean semantic processing and has been researched for long time. Recently, machine learning approaches have demonstrated good results in accuracy and speed. Other knowledge-based approaches are being researched for untrained words. This paper proposes a hybrid method based on the machine learning approach that uses a lexical semantic network. The use of a hybrid approach creates an additional corpus from subcategorization information and trains this additional corpus. A homograph tagging phase uses the hypernym of the homograph and an additional corpus. Experimentation with the Sejong Corpus and UWordMap demonstrates the hybrid method is to be effective with an increase in accuracy from 96.51% to 96.52%.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.77-86
• /
• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• Journal of Digital Convergence
• /
• v.19 no.6
• /
• pp.259-264
• /
• 2021

Recently, as the number of used trading sites supporting used trading increases, users want to search for a variety of information in real time. This new change has enabled a new type of C2C (Commerce to Commerce) transaction in the e-commerce base. However, since each used trading site has its own characteristics, it is difficult to standardize the whole. Therefore, in this paper, we studied a system that provides the transaction data used by the user in real time and provides the desired information quickly. In this paper, we researched the crawler system necessary for the development of the integrated trading system for used goods through Internet e-commerce, and made it possible to provide information in the web environment desired by the user through the defined morpheme analyzer. Therefore, in this study, we designed a system that provides information desired by users without accessing various used goods sites.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.32 no.1
• /
• pp.133-150
• /
• 2021

The purpose of this study is to identify the applicability of machine learning targeting titles in the classification of books in public libraries. Data analysis was performed using Python's scikit-learn library through the Jupiter notebook of the Anaconda platform. KoNLPy analyzer and Okt class were used for Hangul morpheme analysis. The units of analysis were 2,000 title fields and KDC classification class numbers (300 and 600) extracted from the KORMARC records of public libraries. As a result of analyzing the data using six machine learning models, it showed a possibility of applying machine learning to book classification. Among the models used, the neural network model has the highest accuracy of title classification. The study suggested the need for improving the accuracy of title classification, the need for research on book titles, tokenization of titles, and stop words.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.259-266
• /
• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.11
• /
• pp.441-448
• /
• 2019

Previous researches on automatic spacing of Korean sentences has been researched to correct spacing errors by using n-gram based statistical techniques or morpheme analyzer to insert blanks in the word boundary. In this paper, we propose an end-to-end automatic word spacing by using deep neural network. Automatic word spacing problem could be defined as a tag classification problem in unit of syllable other than word. For contextual representation between syllables, Bi-LSTM encodes the dependency relationship between syllables into a fixed-length vector of continuous vector space using forward and backward LSTM cell. In order to conduct automatic word spacing of Korean sentences, after a fixed-length contextual vector by Bi-LSTM is classified into auto-spacing tag(B or I), the blank is inserted in the front of B tag. For tag classification method, we compose three types of classification neural networks. One is feedforward neural network, another is neural network language model and the other is linear-chain CRF. To compare our models, we measure the performance of automatic word spacing depending on the three of classification networks. linear-chain CRF of them used as classification neural network shows better performance than other models. We used KCC150 corpus as a training and testing data.