• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.215-221
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• 2011

Mobile phones, which were used for simply calling and sending text messages, have recently moved to application-oriented digital devices such as smart phones and tablet phones. The rapid increase of smart and tablet phones which can offer advanced ability and run a variety of applications based on Java requires various digital multimedia content activities. These days, there are more than 2.2 billions of Christians around the world. Among them, more than 300 millions of people live in Asian, and all of them have and read the bible. If there is an application for the bible which translates from English to their own languages, it could be very helpful. With this reason, this paper proposes a multi-bible application that supports various languages. To do this, we implemented an algorithm that recognize sentences in the bible as word by word. The algorithm is essentially composed of the following three functions: tokenizing sentences in the bible into word by word (word tokenization), recognizing words by using touch event (word recognition), and translating the selected words to the desired language. Consequently, the proposed multi-bible application supports language translation efficiently by touching words of sentences in the bible.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.36-48
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• 2020

App market has continuously been growth since its launch. The market revenues will reach about 1,000 billion US dollars in 2019. App is a core service for smartphone. Currently, there are more than 1.5 million mobile apps in App platform calling out for attention. So, if you are looking at developing a successful app, you need to have a solid marketing and distribution strategy. Online word of mouth(eWOM) is one of the most effective, powerful App marketing method. eWOM affect potential consumers' decision making, and this effect can spread rapidly through online social network. Despite the increasing research on word of mouth, only few studies have focused on content analysis. Most of studies focused on the causes and acceptance of eWOM and eWOM performance measurement. This study aims to content analysis of mobile apps review In 2013, Google researchers announced Word2Vec. This method has overcome the weakness of previous studies. This is faster and more accurate than traditional methods. This study found out the relationship between mobile app reviews and checked for reactions by Word2vec.

• International Journal of Contents
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• v.6 no.4
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• pp.90-100
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• 2010

As the reuse of content becomes a strategy for the entertainment industry, managerial insight on customers is needed to be cultivated in order to run a successful business. This study analyzes the impact of previous satisfaction on consumption intention for content in another medium. To do this, two data sets are collected: cases of movie-to-TV series and TV series-to-movie and analyze them using a structural equation modeling approach. The results of our analysis show that satisfied viewers of a movie tend to communicate their positive feelings via word-of-mouth communication and demonstrate repurchase intention of another medium afterward. However, satisfaction does not automatically lead to repurchase intention in another medium. While satisfied viewers of a TV series show a statistically positive repurchase intention for a movie, satisfied viewers of a movie do not show a direct repurchase intention for a TV series. This result demonstrates an asymmetric relationship between satisfaction and repurchase intention across media, and its strategic implications are further discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1091-1107
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• 2019

The purpose of this study is to technology impact assessment uncertainties and influences of factors for the coexistence technology in MR and to derive the ripple effect of the target technology in many different application fields. This survey is to evaluate the impact of new technologies on economy, society, culture, ethics and environment in advance and to reflect the results in policy research. As a result, we analyzed the positive and negative impacts predicted by the coexistence technology in MR in edutainment field by word cloud analysis. The purpose of these study is to strengthen the intrinsic intent of technology assessment on the edutainment fields to strengthen positive impacts and minimize adverse impacts by identifying the various factors affecting society when the coexistence technology in MR was commercialized as well as by evaluating the positive and negative impacts from a more balanced view.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.15-24
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• 2010

In this paper, we propose an image classification which combines semantic relations of tags with contents of images to improve the satisfaction of image retrieval on application domains as huge image sharing sites. To make good use of image retrieval or classification algorithms on huge image sharing sites as Flickr, they are applicable to real tagged Web images. To classify the Web images by 'bag of visual word' based image content, our algorithm includes training the category model by utilizing the preliminary retrieved images with semantically related tags as training data and classifying the test images based on PLSA. In the experimental results on the Flickr Web images, the proposed method produced the better precision and recall rates than those from the existing method using tag information.

• The Journal of Information Systems
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• v.29 no.2
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• pp.71-94
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• 2020

Purpose With the development of Social Network Service(SNS) and mobile devices, many companies have been using the Facebook as a Word-of-Mouth(WOM) channel. This study examines the effects of psychological distance and message type on WOM using the Facebook's real messages. And the moderating effect of the message type on the relationship between psychological distance and WOM was also analyzed. Design/methodology/approach A content analysis was used as a research method. A total 7,483 messages were collected from 50 companies' Facebook Fanpage (based on the ranking of socialbakers.com) and content analysis was conducted using human coding. As the influencing variables, the message type and psychological distance and the number of 'Likes', 'Share', and 'Comment' were used as the dependent variable. The R3.4.4 was used to perform descriptive statistics, cross-tab analysis, and analysis of variance(ANOVA). Findings First, a larger proportion of Facebook messages have close psychological distance for all message types(information, advertisement, event, and customer relationship). Second, 'Like' and 'Comment' number were significantly higher in messages of close psychological distance. Third, the effects of psychological distance on 'Like', 'Share', and 'Comment' number were different according to message type. However, 'advertisement' message type had significantly more numbers for all WOMs('Like', 'Share', and 'Comment') in messages with close psychological distance.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.19-25
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• 2020

This study examined thirty-one Korean students' pronunciation of an English passage using a speech recognition application, Speechnotes, and two Canadian raters' evaluations of their speech according to the International English Language Testing System (IELTS) band criteria to assess the possibility of using the application as a teaching aid for pronunciation education. The results showed that the grand average percentage of correctly recognized words was 77.7%. From the moderate recognition rate, the pronunciation level of the participants was construed as intermediate and higher. The recognition rate varied depending on the composition of the content words and the function words in each given sentence. Frequency counts of unrecognized words by group level and word type revealed the typical pronunciation problems of the participants, including fricatives and nasals. The IELTS bands chosen by the two native raters for the rainbow passage had a moderately high correlation with each other. A moderate correlation was reported between the number of correctly recognized content words and the raters' bands, while an almost a negligible correlation was found between the function words and the raters' bands. From these results, the author concludes that the speech recognition application could constitute a partial aid for diagnosing each individual's or the group's pronunciation problems, but further studies are still needed to match human raters.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.11-13
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• 2006

The present paper focuses on the interaction between lexical-semantic information and affective prosody. More specifically, we explore whether affective prosody influence on evaluation of affective meaning of a word. To this end, we asked participants to listen a word and to evaluate the emotional content of the word which were recoded with affective prosody. Results showed that first, emotional evaluation was slower when the word meaning is negative than when they is positive. Second, when the prosody of words is negative, evaluation time is faster than when it is neutral or positive. And finally, when the affective meaning of word and prosody is congruent, response time is faster than it is incongruent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2115-2130
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• 2022

With the development of over-the-top (OTT) services, the demand for content is increasing, and you can easily and conveniently acquire various content in the online environment. As a result, copyrighted content can be easily copied and distributed, resulting in serious copyright infringement. Some special forms of online service providers (OSP) use filtering-based technologies to protect copyrights, but illegal uploaders use methods that bypass traditional filters. Uploading with a title that bypasses the filter cannot use a similar search method to detect illegal content. In this paper, we propose a technique for profiling the Heavy Uploader by normalizing the bypassed content title and efficiently detecting illegal content. First, the word is extracted from the normalized title and converted into a bit-array to detect illegal works. This Bloom Filter method has a characteristic that there are false positives but no false negatives. The false positive rate has a trade-off relationship with processing performance. As the false positive rate increases, the processing performance increases, and when the false positive rate decreases, the processing performance increases. We increased the detection rate by directly comparing the word to the result of increasing the false positive rate of the Bloom Filter. The processing time was also as fast as when the false positive rate was increased. Afterwards, we create a function that includes information about overall piracy and identify clustering-based heavy uploaders. Analyze the behavior of heavy uploaders to find the first uploader and detect the source site.