• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.277-291
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• 2008

In order to carry out river basin management, it is necessary to evaluate the state of the river basin and make site-specific measures on the basis of management goals and objectives. A river basin is divided into several watersheds, which are composed of several components: water resources, social and economic systems, law and institution, user, land, ecosystems, etc. They are connected among them and form network holistically. In this study, a methodology for evaluating watershed management was developed by consideration of the various features of a watershed system. This methodology employed factor analysis to develop sub-indexes for evaluating water use management, environment and ecosystem management, and flood management in a watershed. To do this, first, the related data were gathered and classified into six groups that are the components of watershed systems. Second, in all sub-indexes, preliminary tests such as KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy and Bartlett's test of sphericity were conducted to check the data's acceptability to factor analysis, respectively. Third, variables related to each sub-index were grouped into three factors by consideration of statistic characteristics, respectively. These factors became indicators and were named, taking into account the relationship and the characteristics of included variables. In order to check the study results, the computed factor loadings of each variable were reviewed, and correlation analysis among factor scores was fulfilled. It was revealed that each factor score of factors in a sub-index was not correlated, and grouping variables by factor analysis was appropriate. And, it was thought that this indicator system would be applied effectively to evaluating the states of watershed management.