• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• KIEAE Journal
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• v.7 no.1
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• pp.81-93
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• 2007

Due to increasing awareness about the risk which will be caused by fast aging of population, the importance of aging friendly environment including housing has been paid much attention both individually and socially. In this regard, recently, in Korea, diverse elderly living facilities have increased in its number. Because of little experience, however, there have not been enough holistic understanding about aging friendly housing. Accordingly, most previous literature which introduced elderly housing to Korean society have translated differently, thereby caused more confusion. To facilitate communication about aging friendly housing, clear and consistent definition of its type and comprehensive range needs to be delineated. The purpose of this study is to classify various elderly housing alternatives in architecturally understandable way. This study was proceeded by content analysis of existing literature on elderly housing environment. A comprehensive review on the concept and existing classification of elderly housing was done prior to main analysis of translated Korean words. After classifying the Korean words of definition, systematic classification which focused on two important criteria of determining physical characteristics, such as space sharing degree and intentional plannedness was delineated and suggested. This research shows the first step toward the theoretical foundation for elderly housing classification.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.4
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• pp.529-538
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• 1993

This study was intended to identify the hierarchy of clothing images, which is expected to be helpful in style classification and product positioning. A questionnaire consisted of 110 words expressing clothing images was developed, and eight clothing photographs were selected as stimuli. 289 female subjects aged between 22 to 37 responded to two of the eight photographs during September, 1991. 110 words were reduced to 62 words based on their independence before conducting factor analysis to identify the constructing factors of clothing images. Nine words with negative connotations were eliminated, because they are not sought in product development. To explain the hierarchy of clothing images, cluster analysis was applied. To observe the association of 53 words, dendrogram was introduced, and to interpret the result, eleven sub clusters were determined. This 11 clusters were continuously combined according to their similarities, until they integrated into one 'clothing image'. Two major division of image clusters were 'graceful and feminine image', and 'mannish and simple image'.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.42-48
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• 2016

Since a variety of sound occur in same place and similar sound occurs in other places, the performance of acoustic scene classification is not guaranteed in case of insufficient training data. A Bag of Words (BOW) based histogram feature is foreseen as a method to overcome the problem. However, since the histogram features is made by using a feature distribution, the ordering of sequence of features is ignored. A temporal information such as periodicity and stationarity are also important for acoustic scene classification. In this paper, temporal features about a periodicity and a stationarity are extracted by using a recurrent quantification analysis. In the experiment, performance of the proposed method is shown better than other baseline methods.

• Smart Media Journal
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• v.9 no.1
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• pp.51-59
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• 2020

From PC communication to the development of the internet, a new term has been coined on the social media, and the social media culture has been formed due to the spread of smart phones, and the newly coined word is becoming a culture. With the advent of social networking sites and smart phones serving as a bridge, the number of data has increased in real time. The use of new words can have many advantages, including the use of short sentences to solve the problems of various letter-limited messengers and reduce data. However, new words do not have a dictionary meaning and there are limitations and degradation of algorithms such as data mining. Therefore, in this paper, the opinion of the document is confirmed by collecting data through web crawling and extracting new words contained within the text data and establishing an emotional classification. The progress of the experiment is divided into three categories. First, a word collected by collecting a new word on the social media is subjected to learned of affirmative and negative. Next, to derive and verify emotional values using standard documents, TF-IDF is used to score noun sensibilities to enter the emotional values of the data. As with the new words, the classified emotional values are applied to verify that the emotions are classified in standard language documents. Finally, a combination of the newly coined words and standard emotional values is used to perform a comparative analysis of the technology of the instrument.

• Phonetics and Speech Sciences
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• v.8 no.3
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• pp.1-9
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• 2016

This paper investigated the phoneme distribution and phonological processes of orthographic and pronounced phrasal words in light of syllable structure in the Seoul Corpus in order to provide linguists and phoneticians with a clearer understanding of the Korean language system. To achieve the goal, the phrasal words were extracted from the transcribed label scripts of the Seoul Corpus using Praat. Following this, the onsets, peaks, codas and syllable types of the phrasal words were analyzed using an R script. Results revealed that k0 was most frequently used as an onset in both orthographic and pronounced phrasal words. Also, aa was the most favored vowel in the Korean syllable peak with fewer phonological processes in its pronounced form. The total proportion of all diphthongs according to the frequency of the peaks in the orthographic phrasal words was 8.8%, which was almost double those found in the pronounced phrasal words. For the codas, nn accounted for 34.4% of the total pronounced phrasal words and was the varied form. From syllable type classification of the Corpus, CV appeared to be the most frequent type followed by CVC, V, and VC from the orthographic forms. Overall, the onsets were more prevalent in the pronunciation more than the codas. From the results, this paper concluded that an analysis of phoneme distribution and phonological processes in light of syllable structure can contribute greatly to the understanding of the phonology of spoken Korean.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.425-440
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• 2012

As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.351-361
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• 2003

The previous studies to extract features for document through word association have the problems of updating profiles periodically, dealing with noun phrases, and calculating the probability for indices. We propose more effective feature extraction method which is using association word mining. The association word mining method, by using Apriori algorithm, represents a feature for document as not single words but association-word-vectors. Association words extracted from document by Apriori algorithm depend on confidence, support, and the number of composed words. This paper proposes an effective method to determine confidence, support, and the number of words composing association words. Since the feature extraction method using association word mining does not use the profile, it need not update the profile, and automatically generates noun phrase by using confidence and support at Apriori algorithm without calculating the probability for index. We apply the proposed method to document classification using Naive Bayes classifier, and compare it with methods of information gain and TFㆍIDF. Besides, we compare the method proposed in this paper with document classification methods using index association and word association based on the model of probability, respectively.