• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.95-118
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• 2021

As the AI speaker business has risen significantly in recent years, the potential for numerous uses of AI speakers has gotten a lot of attention. Consumers have created an environment in which they can express and share their experiences with products through various channels, resulting in a large number of reviews that leave consumers with a variety of candid opinions about their experiences, which can be said to be very useful in analyzing consumers' thoughts. Using this review data, this study aimed to examine the factors driving the continued use of AI speakers. Above all, it was determined whether the seven characteristics associated with the intention to adopt AI identified in prior studies appear in consumer reviews. Based on customer review data on Amazon.com, text mining and social network analysis were utilized to examine Amazon eco-products. CONCOR analysis was used to classify words with similar connectivity locations, and Connection centrality analysis was used to classify the factors influencing the continuous use of AI speakers, focusing on the connectivity between words derived by classifying review data into positive and negative reviews. Consumers regarded personality and closeness as the most essential characteristics impacting the continued usage of AI speakers as a result of the favorable review survey. These two parameters had a strong correlation with other variables, and connectedness, in addition to the components established from prior studies, was a significant factor. Furthermore, additional negative review research revealed that recognition failures and compatibility are important problems that deter consumers from utilizing AI speakers. This study will give specific solutions for consumers to continue to utilize Amazon eco products based on the findings of the research.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.105-117
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• 2014

Acquisition of information through the broadcast media is essential for modern life and each broadcaster has progressed its service over the internet with the development of digital technology. Under this circumstance, this study presented the results which compared and analyzed the web accessibility evaluation for Korea, USA, and Japan's leading broadcaster web sites. According to the study results, there was no significant difference in the level of accessibility in all web sites of three countries, but accessibility compliance rate such as alternate text, skip-navigation of repeated region, and title was somewhat insufficient for Korean web sites. In addition, accessibility errors in the brightness contrast of the text contents, the run of the functions that a user doesn't have any intention, the clear statement of the default language, and the label provision were investigated. Therefore, Korean broadcasters should urgently improve and modify these errors and problems for effective web accessibility.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.105-113
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• 2006

In this paper we propose wear-UCAM, which is a toolkit for context-aware application model in wearable computing. As the rapid developments of mobile technologies and relevant technologies, the interests in wearable computer also become indispensible in both academic and industrial fields. However, there are few research activities on the application framework or toolkit for wearable computing. Hence, we suggest wear-UCAM as the development toolkit for wearable computing applications, where we focus on how to collect relevant user context, manage it, and provide services based on the recognized context. The proposed wear-UCAM includes the abstraction of context processing as well as independence among sensors and services with other components. For the sake of rapid prototyping of the proposed toolkit, we utilize PDA with wireless LAN as a wearable computer. The detail explanation of the implementation and its discussion are presented in this paper.

• Journal of East-West Nursing Research
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• v.26 no.2
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• pp.118-129
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• 2020

Purpose: The purpose of this study was to analyze the topic trend of domestic simulation nursing education research using text network analysis(TNA). Methods: This study was conducted in four steps. TNA was performed using the NetMiner (version 4.4.1) program. Firstly, 245 articles from 4 databases (RISS, KCI, KISS, DBpia) published from 2008 to 2018, were collected. Secondly, keyword-forms were unified and representative words were selected. Thirdly, co-occurrence matrices of keywords with a frequency of 2 or higher were generated. Finally, social network-related measures-indices of degree centrality and betweenness centrality-were obtained. The topic trend over time was visualized as a sociogram and presented. Results: 178 author keywords were extracted. Keywords with high degree centrality were "Nursing student", "Clinical competency", "Knowledge", "Critical thinking", "Communication", and "Problem-solving ability." Keywords with high betweenness centrality were "CPR", "Knowledge", "Attitude", "Self-efficacy", "Performance ability", and "Nurse." Over time, the topic trends on simulation nursing education have diversified. For example, topics such as "Neonatal nursing", "Obstetric nursing", "Pediatric nursing", "Blood transfusion", "Community visit nursing", and "Core basic nursing skill" appeared. The core-topics that emerged only recently (2017-2018) were "High-fidelity", "Heart arrest", "Clinical judgment", "Reflection", "Core basic nursing skill." Conclusion: Although simulation nursing education research has been increasing, it is necessary to continue studies on integrated simulation learning designs based on various nursing settings. Additionally, in simulation nursing education, research is required not only on learner-centered educational outcomes, but also factors that influence educational outcomes from the perspective of the instructors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.63-70
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• 2013

In previous research, we suggested a business process structured query language(BPSQL) for information extraction and retrieval in the business process framework, and used an existing query language with the tablization for each layer within the framework, but still had a problem to manually build with the specification of each layer information of BFP. To solve this problem, we suggest automatically to build the schema based business process model with model-to-text conversion technique. This procedure consists of 1) defining each meta-model of the entire structure and of database schema, and 2) also defining model transformation rules for it. With this procedure, we can automatically transform from defining through meta-modeling of an integrated information system designed to the schema based model information table specification defined of the entire layer each layer specification with model-to-text conversion techniques. It is possible to develop the efficiently integrated information system.

• Education of Primary School Mathematics
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• v.23 no.1
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• pp.1-26
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• 2020

The purpose of this study was to analyze the process of reading and understanding mathematics lesson plan through eye-tracking to suggest implications of pre-service teacher education. As a result of the analysis, the pre-service teachers felt that the mathematics lesson plans were more difficult than the experienced teacher, they read and understood the mathematics lesson plan in sequential order. Experienced teachers, on the other hand, used a hypertext reading strategy to find key topics and make connections in order to grasp the flow of instruction in mathematics lesson plan. Based on these results, several suggestions were drawn for pre-service teachers when teaching their ability to read and understand mathematics lesson plan.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.3
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• pp.83-108
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• 2017

As a public institution, it is the duty and responsibility of a library to provide readers with information about books. To provide such information to readers, a librarian should have the ability to provide satisfactory consultation and advice. As interest in picture books grows, there is an increasing need for libraries and librarians to produce and release picture book reviews on a regular basis to inform readers of what makes a good picture book, and which among similar books should they choose. Other than summarizing the content of a picture book, a good picture book review should also provide additional information that will help readers understand the reviewed book such as the literary symbolism of a book's visual texts. To this end, a librarian is required to develop visual literacy, which would enable him/her to read into the deeper meaning of the images imbued with the writer's ideas and philosophy. In light of the above discussion, this study compares and analyzes two picture book versions of the story Little Red Riding Hood, with the purpose of helping librarians understand the visual texts that they need to refer to when writing picture book reviews.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.71-85
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• 2015

Recently, the South Korean government actively opens the public data to encourage people to use it in private sector. It is based on 'Government 3.0' that is the paradigm for government operation. According to this trend, a data platform is required for establishing and commercializing business models that utilizing geospatial data. However, it is currently insufficient to establish the geospatial data system using the text-based public open data. This study constructs a geospatial data supply system using the public data for the purpose of providing and using the public data of spatial reference type efficiently. It improves the accessibility of user and the usability to the public data having location information. Besides, this study suggests that the components of the appliance system that connects the public data from different public institutions for different purposes with producing the geospatial data in the type of a finished product.