• The Journal of Information Systems
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• v.31 no.4
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• pp.189-207
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• 2022

Purpose Previous research on customer engagement in SNS marketing has mainly addressed the conceptualization of type of word-of-mouth messages. However, there is a lack of researches about the effect of the brand-page characteristics on the type of word-of-mouth messages. Therefore, this study examined the effect of brand-page characteristics in terms of the type of word-of-mouth messages as the main objective and the effect of the type of word-of-mouth messages in terms of the brand loyalty as the secondary objective in the context of Facebook. Design/methodology/approach The empirical research was based on a poll done through 400 research candidates in the Facebook and the final 342 responses were collected and used in statistical data analysis. The adaptability, trust, and validity to measurement model were verified and the structural relationship in the research model was analyzed through these 342 responses. The collected data verified hypotheses established using the SPSS statistical package and structural equation model using AMOS. Findings The results showed that the BP-information provision had a non-significant effect on the factual word-of-mouth message and a significant effect on the evaluative word-of-mouth message. The BP-reliability had a significant effect on the factual word-of-mouth message and the evaluative word-of-mouth message. The BP-entertainment had a significant effect on the factual word-of-mouth message and the evaluative word-of-mouth effect. The BP-interaction had a non-significant effect on the factual word-of-mouth message and the evaluative word-of-mouth message. Finally, the factual word-of-mouth message and the evaluative word-of-mouth message had a significant effect on the brand loyalty.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.47-57
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• 2019

We examine the weaknesses of the existing OOXML-based MS-Word file structure, and analyze how data concealment and forgery are performed in MS-Word digital documents. In case of forgery by including hidden information in MS-Word digital document, there is no difference in opening the file with the MS-Word Processor. However, the computer system may be malfunctioned by malware or shell code hidden in the digital document. If a malicious image file or ZIP file is hidden in the document by using the structural vulnerability of the MS-Word document, it may be infected by ransomware that encrypts the entire file on the disk even if the MS-Word file is normally executed. Therefore, it is necessary to analyze forgery and alteration of digital document through internal structure analysis of MS-Word file. In this paper, we designed and implemented a mechanism to detect this efficiently and automatic detection software, and presented a method to proactively respond to attacks such as ransomware exploiting MS-Word security vulnerabilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.124-129
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• 2006

This parer presents a method of ranking translation word selection for Korean verbs based on lexical knowledge contained in a bilingual Korean-English dictionary and WordNet that are easily obtainable knowledge resources. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness through the 45 extended relations between possible translations of target word and some indicative clue words that play a role of predicate-arguments in source language text. In order to reduce the weight of application of possibly unwanted senses, we rank the possible word senses for each translation word by measuring semantic similarity between the translation word and its near synonyms. We report an average accuracy of $51\%$ with ten Korean ambiguous verbs. The evaluation suggests that our approach outperforms the default baseline performance and previous works.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.7-8
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• 2018

This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.745-748
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• 2014

SentiWordNet is an important lexical resource supporting sentiment analysis in opinion mining applications. In this paper, we propose a novel approach to construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated from WordNet in which each synset has numerical scores to indicate its opinion polarities. Many previous studies obtained these scores by applying a machine learning method to WordNet. However, Vietnamese WordNet is not available unfortunately by the time of this paper. Therefore, we propose a method to construct VSWN from a Vietnamese dictionary, not from WordNet. We show the effectiveness of the proposed method by generating a VSWN with 39,561 synsets automatically. The method is experimentally tested with 266 synsets with aspect of positivity and negativity. It attains a competitive result compared with English SentiWordNet that is 0.066 and 0.052 differences for positivity and negativity sets respectively.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.61-74
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• 2019

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a "prescription generating game" and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1255-1258
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• 1998

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. This method doesn't take account of discrimination to other words. To complement this problem, this paper proposes a word verification method by re-recognition of the recognized word and its similar word using the discriminative function between two words. The similar word is selected by calculating the probability of other words to each HMM. The recognizer haveing discrimination to each word is realized using the weighting to each state and the weighting is calculated by genetic algorithm.

• MALSORI
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• no.54
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• pp.15-26
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• 2005

The aim of this paper was to analyze the lexical effects on spoken word recognition of Korean monosyllabic word. The lexical factors chosen in this paper was frequency, density and lexical familiarity of words. Result of the analysis was as follows; frequency was the significant factor to predict spoken word recognition score of monosyllabic word. The other factors were not significant. This result suggest that word frequency should be considered in speech perception test.

• The Pure and Applied Mathematics
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• v.15 no.4
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• pp.353-364
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• 2008

The purpose of this paper is to investigate the finite extension of weighted word L-delta groups. The paper revealed that a finite extension of a weighted word L-delta group is a weighted word L-delta group, and an abelian group, in addition, is a weighted word L-delta group and simultaneously a word L-delta group.

• Phonetics and Speech Sciences
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• v.6 no.1
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• pp.47-58
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• 2014

The purpose of this paper is to find out how Korean speakers' speech production in English word boundaries differs from English speakers' and to account for what bring about such differences. Seeing two consecutive words as one single cluster, the English speakers generally pronounce them naturally by linking a word-final consonant of the first word with a word-initial vowel of the second word, while this is not the case with most of the Korean speakers; they read the two consecutive words individually. In consequence, phonological processes such as resyllabification and aspiration can be found in the English speakers' word-boundary production, while glottalization, and unreleased stops are rather common phonological process seen in the Korean speakers' word-boundary production. This may be accounted for by Korean speakers' L1 interference, depending on English proficiency.