5,105 research outputs found

    인공지능 관련 뉴스 기사의 프레임, 감정 분석

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    학위논문(석사) -- 서울대학교대학원 : 사회과학대학 언론정보학과, 2022. 8. 이철주 .This study examines how artificial intelligence (AI) is presented in the news media by examining the frames and emotions expressed in news coverage about AI. For analysis, I used computational text analysis techniques -structural topic model (STM) to extract frames and NRC Emotion Lexicon and Linguistic Inquiry and Word Count (LIWC) to detect emotions. Then I examined their correlations with the political ideology of media outlets (conservative vs. liberal) and media type (newspapers vs TV news). By identifying the frames and the emotions embedded in the news media, it would be possible to predict how they influence the formation of public opinions and attitudes towards AI.본 연구는 컴퓨터 텍스트 분석 기술을 통해 인공지능 (AI)에 대한 뉴스 보도에 드러난 프레임과 감정을 분석하여 인공지능이 뉴스 미디어에서 어떻게 표현되는지를 살펴보는 것을 목적으로 한다. 프레임 추출을 위해 Structural Topic Model (STM) 기법을, 감정 추출을 위해 NRC Emotion Lexicon과 Linguistic Inquiry and Word Count (LIWC) 프로그램을 활용했다. 언론사의 정치 성향(보수 – 진보)과 미디어 유형(신문 – 방송)을 변수로 설정해, 추출된 결과와 각 변수와의 상관관계를 분석했다. 뉴스 미디어에 내재된 프레임과 감정을 파악함으로써, 그것이 AI에 대한 여론 및 태도 형성에 어떤 영향을 미치는지 예측할 수 있을 것이다.Chapter 1. Introduction 1 Chapter 2. Literature Review and Research Aim 2 Chapter 3. Conceptual Framework 8 Chapter 4. Methods 21 Chapter 5. Results 27 Chapter 6. Discussion 45 Appendix. 49 Bibliography. 51 Abstract in Korean. 57석

    A semantic approach to analyze scientific paper abstracts

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    International audienceEach domain and its underlying communities evolve in time and each period is centered on specific topics that emerge from textual sources that characterize the domain. Our analysis represents an extension of other researches performed on the same corpora that were focusing more on evaluating co-citations between the articles in order to compute their importance score (Grauwin and Jensen [1]). Our approach presents a general perspective of the domain by performing semantic comparisons between article abstracts using natural language processing techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or semantic distances in lexicalized ontologies, i.e. WordNet. Moreover, graph visual representations are generated using Gephi in order to highlight the keywords of each paper and of the domain, the document similarity view or the table of keyword-abstract overlap score. The purpose of the views is to minimize the learning curve of the domain and to facilitate the research process for someone interested in a particular subject. Also, in order to further argue the benefits of our approach, some potential refinements of the methods for classification that can be performed as future improvements are presented

    Analyzing the Semantic Relatedness of Paper Abstracts: An Application to the Educational Research Field

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    International audienceEach domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-today activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., WordNet. Moreover, the defined mechanisms are enforced on two different subdomains from the corpora generated around the keywords " e-learning " and " computer ". Graph visual representations are used to highlight the keywords of each subdomain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework

    Modeling media as latent semantics based on cognitive components

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    Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy

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    International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure

    Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy

    No full text
    International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure

    The use of social media in EU policy communication and implications for the emergence of a European public sphere

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    Cohesion policy is the European Union’s (EU) main investment policy and seeks to strengthen economic, social and territorial cohesion. While accomplishments in this regard are constantly measured, European citizens are not always aware of the policy’s impact and the role the EU plays therein. This is especially relevant as the communication of EU policies is central to the emergence of a European public sphere, an acknowledged condition for European integration. In this paper, we aim at advancing research in this regard through the analysis of cohesion policy communication on the social media channels of ten Local Managing Authorities (LMAs) responsible for managing and communicating structural funds at the local level. By building on a bottom-up construction of shared meaning structures through semi-automatic analysis techniques, we make the following three observations: first, social media communication is indicative of "horizontal Europeanization"; second, Europeanization occurs both in the form of the spontaneous amalgamation of shared discontent expressed by citizens and the institutionalization of top-down EU communication measures adopted by LMAs; and third, a cluster of topics articulated internationally and showcasing a negative attitude towards the EU funding scheme suggests that, counter-intuitively, Euroscepticism seems to facilitate the emergence of a European public sphere
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