• Journal of Educational Research in Mathematics
• /
• v.12 no.1
• /
• pp.95-108
• /
• 2002

In this article we drew out five definition-methods in school geometry. They are called synonymous method, denotative method, analytic method. And we analyzed them theoretically. On our analysis we tried to identify the level of common sense and the level of science in definition of those two levels on the definition-methods of circle. While the definition-method in elementary school could be regarded as the level of common sense, that in middle school could be considered as the level of science. Finally, we made the following didactical comments. Definitions in school mathematics might have the levels as regard to their roles. Thus, Mathematics teachers, curriculum developers, and text authors all need to recognize the subtle differences in the level of definition-methods.

• Journal of Wellness
• /
• v.7 no.2
• /
• pp.25-37
• /
• 2012

The purpose of this study was to analyze the mass media's news framing on the elderly leisure. For this, researchers collected news articles from daily newspapers such as Chosun Ilbo, Hankyoreh, Kyunghyang Shinmun, and analysed the news framing of them through text-analytic approach. Total of 153 news the about elderly leisure between 1990 and 2010 were used for frame analysis. The Frame analyses were divided by formal frame and content frame. The formal frame was formed by deduction based on the classification method of Iyergar(1991) and the content frame was formed by induction according to the analysis of overall themes and titles of news. The main result could be summarized as follows: First, the analysis of formal frame showed that the episodic frame was predominantly high which focused on specific event or occurrences about elderly leisure. Second, it was found by content frame analysis that elderly leisure's facilities, health, serious leisure, policy frame were main subjects of news framing.

• The Journal of the Korea Contents Association
• /
• v.21 no.10
• /
• pp.184-193
• /
• 2021

Interactive documentary refers to a new style of documentary that is created and accepted through active interaction. It is attracting attention as a platform that forms a public sphere and mediates audiences to participate in social change. However, the possibilities was not systematically explored, and there was insufficient consideration on how to realize them. In this paper, discussions on the political aesthetics of Walter Benjamin are examined, and the media characteristics of interactive documentary are analyzed through text mining. Then, by connecting the two to each other, we draw a map of the political and aesthetic possibilities, and based on the map, we analyze the actual works. This study has the value of establishing a theoretical framework for the possibilities of interactive documentaries. In the follow-up study, we will consider the practical strategy of interactive documentary as a transmedia activism and develop a practical analysis and planning methodology.

• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.69-75
• /
• 2023

Purpose: The risk of disaster from extreme weather events is increasing due to the increase in occurrence and the strength of heavy rains and storms from continued climate change. To reduce these risks, emergency weather information customized for the characteristics of the information users and related circumstances should be provided. Method: A first-stage emergency weather information delivery system has been developed to provide weather information to the disaster-risk area residents and the disaster response personnel. Novel methods to apply artificial intelligence to identify emergencies have been studied. The relationship between special weather reports from meteorological administration and disaster-related news articles has been analyzed to identify the significance of a pilot study using text analytic artificial intelligence. Result: The basis to identify the significance of the relations between disaster-related articles and special weather reports has been established and the possibility of the development of a real-world applicable system based on a broader analysis of data has been suggested. Conclusion: Through direct alert delivery of weather emergency alerts, a weather emergency alert system is expected to reduce the risk of damage from extreme weather situations.

• Journal of Mechanical Science and Technology
• /
• v.19 no.12
• /
• pp.2197-2204
• /
• 2005

The combination of Gas chromatography (GC) for separation and Flame Ionization Detection (FID) for detection and identification of the components of a mixture of compounds is a fast and strongly proved method of analytic chemistry. The objective of this research was to design a combined High-speed miniature screening Gas chromatograph along with a Flame Ionization Detector for quick, quantitative and qualitative analysis of gas components. This combined GC-FID system is suitable to detect the volatile and semi-volatile hydrocarbons present in a gas mixture. The construction made it less expensive, easy to use and movable. The complete gas path was developed. On/off valves, temperature and flow sensors and their interface electronics were used for controlling purpose. A Microcontroller was programmed to measure the temperature and gas flow using the sensors and to control and regulate them using the electronics and valves. A pocket PC with its touch screen served as a user interface for the system. Software was developed for the pocket PC, which makes the communication possible with the Microcontroller. The system parameters can be indicated in the Pocket PC as simple text and also the analysis result can be displayed.

• Journal of Technology Innovation
• /
• v.25 no.3
• /
• pp.1-24
• /
• 2017

With the rise of internet of things (IoT), there have been several studies to analyze the technological trend and technological convergence. However, previous work have been relied on the qualitative work that investigate the IoT trend and implication for future business. In response, this study considers the patent information as the proxy measure of technology, and conducts a quantitative and analytic approach for analyzing technological convergence using patent co-classification analysis and text mining. First, this study investigate the characteristics of IoT business, and characterize IoT business into four dimensions: device, network, platform, and services. After this process, total 923 patent classes are classified into four types of IoT technology group. Since most of patent classes are classified into device technology, we developed a co-classification network for both device technology and all technologies. Patent keywords are also extracted and these keywords are also classified into four types: device, network, platform, and services. As a result, technologies for several IoT devices such as sensors, healthcare, and energy management are derived as a main convergence group for the device network. For the total IoT network, base network technology plays a key role to characterize technological convergence in the IoT network, mediating the technological convergence in each application area such as smart healthcare, smart home, and smart grid. This work is expected to effectively be utilized in the technology planning of IoT businesses.

• Journal of KIISE:Databases
• /
• v.31 no.4
• /
• pp.395-408
• /
• 2004

A conjunctive Boolean text query refers to a query that searches for tort documents containing all of the specified keywords, and is the most frequently used query form in information retrieval systems. Typically, the query specifies a long list of keywords for better precision, and in this case, the order of keyword processing has a significant impact on the query speed. Currently known approaches to this ordering are based on heuristics and, therefore, cannot guarantee an optimal ordering. We can use a systematic approach by leveraging a database query processing algorithm like the dynamic programming, but it is not suitable for a text query with a typically long list of keywords because of the algorithm's exponential run-time (Ο(n2$^{n-1}$)) for n keywords. Considering these problems, we propose a new approach based on a property called the join sequence separability. This property states that the optimal join sequence is separable into two subsequences of different join methods under a certain condition on the joined relations, and this property enables us to find a globally optimal join sequence in Ο(n2$^{n-1}$). In this paper we describe the property formally, present an optimization algorithm based on the property, prove that the algorithm finds an optimal join sequence, and validate our approach through simulation using an analytic cost model. Comparison with the heuristic text query optimization approaches shows a maximum of 100 times faster query processing, and comparison with the dynamic programming approach shows exponentially faster query optimization (e.g., 600 times for a 10-keyword query).

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.29-44
• /
• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of the Korean Society for Library and Information Science
• /
• v.51 no.4
• /
• pp.77-97
• /
• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• The Journal of the Korea Contents Association
• /
• v.12 no.4
• /
• pp.76-85
• /
• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.