• 디지털산업정보학회논문지
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• 제19권4호
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• pp.87-96
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• 2023

ChatGPT, a leader in generative AI, can use natural expressions like humans based on large-scale language models (LLM). The ability to grasp the context of the language and provide more specific answers by algorithms is excellent. It also has high-quality conversation capabilities that have significantly developed from past Chatbot services to the level of human conversation. In addition, it is expected to change the operation method of the tourism industry and improve the service by utilizing ChatGPT, a generative AI in the tourism sector. This study was conducted to explore ChatGPT trends and paradigms in tourism. The results of the study are as follows. First, keywords such as tourism, utilization, creation, technology, service, travel, holding, education, development, news, digital, future, and chatbot were widespread. Second, unlike other keywords, service, education, and Mokpo City data confirmed the results of a high degree of centrality. Third, due to CONCOR analysis, eight keyword clusters highly relevant to ChatGPT in the tourism sector emerged.

• 한국어교육
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• 제29권4호
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• pp.225-254
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• 2018

The purpose of this study is to contrast the patterns of realization and understanding of refusal speech acts between Korean and Thai learners. This study intends to answer the following questions: (1) Do Koreans and Thai learners perform refusal speech acts differently? (2) Do Koreans and Thai learners understand refusal speech acts differently? A DCT and a follow-up interview were conducted to collect data of two groups of 30 native Korean speakers and 30 native Thai speakers. For research question 1, we analyzed the refusal strategy and provided reasons given by Koreans and Thai learners depending on the context. For research question 2, we ran a chi-squared test on the elements of the follow-up interviews, such as the weight of burden of refusing, and whether the participant would actually refuse or not. The differences between the refusal strategies of the two groups could be categorized by the preceding inducing speech act. In refusing a request, the difference was prominent in the apologizing strategy, whereas in refusing a suggestion, the difference was mainly in the direct refusal strategy. When refusing an invitation, the most evident difference was the number of refusal strategies employed. When providing an explanation of refusal to people with high social status, Koreans gave more specific reasons for refusals, whereas Thai learners tended to use more vague reasons. Moreover, when refusing an invitation, Koreans primarily mentioned the relationship, and Thai learners showed the spirit of Greng Jai. When asked the weight of burden of refusing, Koreans felt pressured to refuse a request from people with high social status, and a suggestion or invitation from people with high level of intimacy while Thai learners found it highly difficult to make a refusal in all cases. In answering whether they would actually refuse or not, Koreans tried not to make a refusal to people with high level of intimacy, and such a trend was not evident among the Thai. This study can help us better understand the learner's pragmatic failure, and serve as a basis in establishing a curriculum for teaching speech acts.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• 영어어문교육
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• 제17권3호
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• pp.27-56
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• 2011

The analysis on the use of lexical discovery and consolidation strategies that have been researched within the area of vocabulary learning strategies (VLS) have not sufficiently drawn the interest of EFL practitioners with regard to vocational high school learners. The results, however, are expected to have implications for the design of vocabulary tasks and instructional materials for EFL learners. The present study investigates EFL vocational high school learners' use of lexical discovery and consolidation strategies with questionnaires, where the use of the learners' lexical discovery strategies were further validated with the think-aloud methodology by asking samples of proficient and less proficient learners to report on their reading process while reading L2 texts that had not been exposed to the learners. The results indicated that there were significant differences between the two groups of learners in the employment of 11 of the strategies which were in the categories of determination, social, memory, and metacognitive strategies, but not for cognitive strategies. The pattern of strategies indicated that different lexical discovery and consolidation strategies were employed relatively more by one proficiency group than another. The study suggests some implications for how strategy-based instruction can be implemented in EFL classrooms.

• 패션비즈니스
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• 제19권6호
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• pp.112-126
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• 2015

In social environments, hashtags have been widely adopted and have become a new form of language for users. The current study attempts to enhance our understanding of users and their motivations to use hashtags when posting fashion-related information. Specifically, this study examines whether user characteristics (fashion leadership, conspicuousness) influence their motivations to use hashtags (curation, self-presentation, information diffusion), which then leads to behavioral intentions to continue to use hashtags and recommend the same to others. An online survey was administered to test our research questions. A total of 136 consumers in their 20s, 30s, and 40s living in Korea were used for data analysis. Structural equation modeling was conducted, which revealed that fashion leadership and conspicuousness had a positive impact on users' motivations of curation, self-presentation, and information diffusion. Motivations of self-presentation and information diffusions were found to affect users' behavioral intentions while curation had no significant impact. Practical implications are presented.

• 정보관리학회지
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• 제38권4호
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• pp.231-250
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• 2021

국내의 외국인 유학생들의 증가에도 불구하고, 유학생들의 건강정보탐색행위에 대한 연구는 부족한 실정이다. 본 연구는 한국에 거주하는 외국인 유학생의 건강정보탐색행위를 설문지와 결정적사건기법을 이용한 면담을 병행하여 조사하였다. 최근 경험한 건강정보 요구 중에는 코로나와 관련된 정보요구(코로나 검사, 증상, 백신)와 병원을 찾는 요구가 가장 많이 보고되었다. 건강정보탐색 과정에서 경험한 어려움으로는 언어의 문제가 가장 많았고, 한국 의료시스템에 대한 이해의 부족, 인터넷상의 정보 불충분 또는 정보과잉 등도 보고되었다. 언어의 장벽에도 불구하고, 유학생들은 한국어 정보원(친구/가족, 웹사이트, 소셜미디어)을 주요 건강정보원으로 사용하였다. 유학생들은 구글번역기를 사용하거나, 이중언어가 가능한 친구/가족의 도움을 받아 한국어 정보원을 통해 건강정보를 탐색하는 것으로 조사되었다. 한국 체류기간이 짧거나 한국어 능력이 부족한 경우, 소셜네트워크 상의 커뮤니티를 통해 건강정보를 얻는 경향을 보이는 반면, 한국 체류기간이 길고 한국어 능력이 좋을수록 웹사이트를 사용하는 것으로 나타났다. 필요한 건강정보를 찾는데 있어 어느 정도 자신감을 가지는가에 대한 질문에는 참가자의 28%만이 긍정적인 대답을 하였다. 본 연구 결과를 통한 유학생들의 건강정보추구행위에 대한 이해를 바탕으로 유학생들이 필요한 건강정보를 탐색하는데 도움을 줄 수 있는 방안에 관한 제언을 제시하였다.

• 한국콘텐츠학회논문지
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• 제16권2호
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• pp.643-651
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• 2016

본 연구의 목적은 영어권 나라의 영화나 드라마가 외국어 학습 자료로서의 가치가 있는지를 공손한 언어를 중심으로 고찰해 보는 것이다. 영어를 학습할 때 의사소통 능력의 강조로 많은 학습자들은 실용적 표현을 습득하는데 중점을 두는 경향이 있다. 하지만 우리는 살아가면서 같은 말이라도 상대방을 기분 좋게 할 수 있고, 상대방의 호의를 거절하거나 불쾌한 정보를 전달해야 할 경우 상대방의 기분을 고려해서 최대한 신중하고 예의바르게 말하는 경우도 있다. 따라서 우리가 사회적 관계를 맺고 살아나가기 위해서 일상생활 속 공손한 언어의 학습은 필수적이다. 이를 위해서 본 연구에서는 영국 드라마 다운튼 애비(Downton Abbey)를 이용하여 이 드라마에서 사용된 공손한 언어를 분석하고 제 2언어 학습 시 공손한 표현을 의식적으로 학습할 필요가 있음을 강조한다. 또한 실제 교실 현장에서는 이러한 공손한 표현들을 실질적으로 어떻게 학습해야 하는지가 중요하다. 다운튼 애비에 나타난 공손한 언어를 장기 기억으로 내재화하기 위한 조별 학습 활동으로 동영상 촬영하기와 캐릭터 파악하기를 제안한다. 다양한 장르와 다채로운 주제를 지닌 영상자료는 학습자가 학습하고자 하는 외국어의 다양한 언어 기능을 모두 보여줄 수 있는 매우 폭넓은 콘텐츠이다. 외국어 학습자라면 영화나 드라마, 시트콤을 자신의 학습 목적에 맞는 학습 자료로 활용한다면 학습의 동기부여와 흥미 증진에 큰 도움이 될 것이다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권11호
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• pp.521-526
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• 2017

데이터는 데이터 형식이 다양하고 방대할 뿐만 아니라 그 생성 속도가 매우 빨라 기존의 데이터 처리 방식이 아닌 새로운 관리 및 분석 방법이 요구된다. 소셜 네트워크 상의 온라인 문서에서 인간의 언어로 쓰여진 비정형 텍스트에서 Text Mining기법을 사용하여 유용한 정보를 추출할 수 있다. 소셜미디어에 남긴 정치, 경제, 문화에 대한 메시지에 대한 경향을 파악하는 것이 어떤 주제에 관심을 가지고 있는지를 파악할 수 있는 요소가 된다. 본 연구에서는 주제 중심 분석 기법을 이용하여 주어진 키워드에 관한 온라인 뉴스를 대상으로 텍스트 마이닝을 수행하였다. LDA(Latent Dirichiet Allocation)를 이용하여 웹문서로부터 정보를 추출하고 이로부터 사람들이 실제로 주어진 키워드에 대하여 어떤 주제에 관심이 있고 관련된 핵심 가치 중 어떤 주제를 중심으로 전파되고 있는지를 분석하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.93-96
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• 2018

데이터를 저장하고 분석하여 새로운 지식을 얻을 수 있는 빅데이터 처리기술은 사회의 여러 분야에서 중요성이 강조되고 있으며 정보통신기술 분야의 핵심 이슈로 부각되면서 관련 기술에 대한 관심이 증가하고 있다. 이러한 빅데이터를 분석할 수 있는 도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 이를 이용하여 성경데이터를 분석한다. R을 이용하여 어떠한 텍스트가 분포되어 있는지를 빈도 조사를 수행하며 소셜 네트워크 분석을 통해 성경을 분석한다.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.