• Journal of the Ergonomics Society of Korea
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• v.32 no.4
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• pp.371-380
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• 2013

Objective: The aim of this study is to investigate the effects of design parameters of vibrotactile signals on semantic association with transmitted information conveying different meanings. Background: As information communication relying on human visual channel becomes excessive, the utility of vibrotactile signals is being interested as a substitute measure of delivering information. Properly designed hapticons may relieve burden of visual communication by rendering distinct and meaningfully compatible haptic sensations. Method: A typical Kansei engineering approach was adopted in this study. Ten most distinctive hapticons were selected among those having different frequencies and amplitudes. Associations between the hapticons and twenty four pairs of adjectives used to describe the state of automobile in control were gathered from thirty subjects using semantic differential scales. Results: The selected pairs of adjectives were summarized by factor analysis into two semantic dimensions named 'Awareness' and 'Directionality'. The experimental hapticons matched with the semantic dimensions were presented as a haptic emotion map. Conclusion: The results from this study support that frequencies and amplitudes of haptic signals play important roles in arousing different human perceptions regarding the two haptic emotional dimensions. Application: Properly designed hapticons with respect to the contents of transmitted information will increase human operator's situation awareness as well as system performance. The result from this study can be used to develop standardized hapticons for active haptic communication.

• Architectural research
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• v.24 no.2
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• pp.41-52
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• 2022

The purpose of this study is to grasp the understanding of smart cities and to review whether the common perception of smart cities, as people understand it, is changing over time. This study analyzes keywords related to smart cities used in social network services (SNSs) in 2011, 2016, and 2020 respectively through semantic network analysis. Smart city discussions appearing on SNS in 2011 mainly focused on technology, and the results of 2016 were generally similar to those of 2011. We can also find policy or business-oriented characteristics in emerging countries in 2020. We highlight that all the results of 2011, 2016, and 2020 have some correlation with each other through QAP(Quadratic Assignment Procedure) correlation analysis, and among them, the correlation between 2011 and 2016 is analyzed the most. The results of the frequency analysis, centrality analysis, and CONCOR(CONvergence of interaction CORrelation) analysis support these results. The results of this study help establish policies that reflect the needs and opinions of citizens in planning smart cities by identifying trends and paradigm transitions expressed by people in SNS. Furthermore, it is expected to help emerging countries by enhancing the understanding of the essence and trend of smart cities and to contribute by suggesting the direction of more sustainable technology development in future smart city policies for leading countries.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1144-1147
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• 1987

This paper designs parser using GPSC for syntactic and semantic analysis of English input sentences. By use of a number of unification-based principles and Tomita's algorithm, syntactic analysis is described. Also in semantic analysis, Montague semantics is used.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.32-40
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• 2004

This study tried to analyze the characteristic data, gathered by the semantic differential method, of respondents, user manuals and agricultural machines with the traditional statistical approach, i.e., cluster analysis and factor analysis semantic differential methods. Though the existing papers of the traditional sensory engineering only suggested the fragmentary result of analysis, this study tries to analyze the data with step-by-step approach, in which this study is analyzing the data with cluster analysis to get the characteristics of respondents, and then using the factor analysis to condensing the adjectives of describing the manual characteristics into several components. Concludingly, this study suggested a prototype of analyzing the semantic differential data with using cluster analysis and factor analysis.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.362-377
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• 2023

The purpose of this study was to investigate the effect of semantic mapping as a science text reading strategy on high school students' inferential understanding. For this purpose, eight science text reading classes were conducted a reading strategy using semantic mapping for 46 students in two science-focused classes in the third grade of a high school. To investigate the effects of semantic mapping reading strategy on students' inferential comprehension, students' pre- and post-reading ability tests results were analyzed. In order to find out the change in inferential comprehension, the level of the inferential comprehension was analyzed using the analysis framework for developed in this study. For the classification of inferential comprehension, the levels of the inferential comprehension were converted into scores. The results of the analysis of changes in students' inferential comprehension showed that semantic mapping reading strategy classes influenced the changes in high school students' inference, especially bridge inference and elaborative inference among sub-elements of inferential comprehension.

• Educational Technology International
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• v.20 no.2
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• pp.169-194
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• 2019

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.10-20
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• 2010

This paper presents chunking strategy of a contiguous nouns sequence using semantic class. We call contiguous nouns which can be treated like a noun the compound noun phrase. We use noun pairs extracted from a syntactic tagged corpus and their semantic class pairs for chunking of the compound noun phrase. For reliability, these noun pairs and semantic classes are built from a syntactic tagged corpus and detailed dictionary in the Sejong corpus. The compound noun phrase of arbitrary length can also be chunked by these information. The 38,940 pairs of 'left noun - right noun', 65,629 pairs of 'left noun - semantic class of right noun', 46,094 pairs of 'semantic class of left noun - right noun', and 45,243 pairs of 'semantic class of left noun - semantic class of right noun' are used for compound noun phrase chunking. The test data are untrained 1,000 sentences with contiguous nouns of length more than 2randomly selected from Sejong morphological tagged corpus. Our experimental result is 86.89% precision, 80.48% recall, and 83.56% f-measure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.213-216
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• 2018

As the advantages of smart devices based on artificial intelligence and voice recognition become more prominent, Virtual Assistant is gaining popularity. Virtual Assistant provides a user experience through smart speakers and is valued as the most user friendly IoT device by consumers. The purpose of this study is to investigate whether there are differences in people's perception of the key virtual assistant brand voice recognition. We collected tweets that included six keyword form three companies that provide Virtual Assistant services. The authors conducted semantic network analysis for the collected datasets and analyzed the feelings of people through sentiment analysis. The result shows that many people have a different perception and mainly about the functions and services provided by the Virtual Assistant and the expectation and usability of the services. Also, people responded positively to most keywords.