• Journal of Information Science Theory and Practice
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• v.9 no.1
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• pp.35-53
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• 2021

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.595-602
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• 2020

We propose a generative probabilistic model with Dirichlet prior distribution for topic modeling and text similarity analysis. It assigns a topic and calculates text correlation between documents within a corpus. It also provides posterior probabilities that are assigned to each topic of a document based on the prior distribution in the corpus. We then present a Gibbs sampling algorithm for inference about the posterior distribution and compute text correlation among 50 abstracts from the papers published by IEEE. We also conduct a supervised learning to set a benchmark that justifies the performance of the LDA (Latent Dirichlet Allocation). The experiments show that the accuracy for topic assignment to a certain document is 76% for LDA. The results for supervised learning show the accuracy of 61%, the precision of 93% and the f1-score of 96%. A discussion for experimental results indicates a thorough justification based on probabilities, distributions, evaluation metrics and correlation coefficients with respect to topic assignment.

• Speech Sciences
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• v.13 no.2
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• pp.87-105
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• 2006

Many researchers have studied the relationship between the information structure and intonation. Arguments about the relations between the information structure and intonation researched so far can be summarized as follows: the intonation of topic and focus in English information structure is represented as i) a pitch accent, ii) a tune (a pitch accent + an edge tone), or iii) a boundary tone. The purpose of this paper is to study various informational patterns of the topic in English information structure, using real TV discussion data. In this paper, the topic is classified as contrastive topics and non-contrastive topics, based on contrastiveness. The results show that the intonation of the topic in English information structure is implemented as a pitch accent, neither a tune nor a boundary tone. Of the non-contrastive topics, while anaphoric determinative NP topics (Lnc, Lncd) are mainly represented as a H* pitch accent, the pronoun topic(Lp) does not have a pitch accent. Of contrastive topics, while the semantically focused topic(Lci) is mainly represented as a H* pitch accent, the contrastively focused topic(Lcc) is represented as both H* and L+H* pitch accents. It shows that it is not always true that the topic or focus to have the meaning of contrast is represented as a L+H* pitch accent as argued in the previous researches.

• Journal of The Korean Association For Science Education
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• v.30 no.5
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• pp.533-543
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• 2010

This paper analyzed the improvement of cognitive level of the students and interactions which occurred in each step of free-topic scientific inquiry to help science teachers understand free-topic scientific inquiry better. Free-topic scientific inquiry is helpful to students with a deep strategic learning goal orientation type or a deep strategic ability goal orientation type in their transition (2B/3A) of cognitive levels. Most students have difficulties in the phase of establishing topics and hypotheses. The result says that the discussion techniques are improved through free-topic scientific inquiry, but the quality of interaction is not easily improved. The deep strategic learning goal orientation type concretizes opinion through interaction in free-topic scientific inquiry. The deep strategic ability goal orientation type are actively involve in the interaction, but they pay no attention to the process because they stick to the result. The surface strategic ability goal orientation type can not deepen a discussion due to high frequency of low level inquiry. However, the frequency of high level inquiry increases through free topic scientific inquiry operation. As a result, the characteristics of free-topic scientific inquiry were discussed and the educational implications of the progress of free-topic scientific inquiry and the organization of grouping were drawn.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.1
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• pp.108-114
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• 2015

The purpose of this study is to find the relationships among communication competency, social network centrality(trust centrality and knowledge sharing centrality), discussion performance, and online boarding activity in the team based learning situation. For investigating this topic, 44 students are participated in the classes of educational technology. In order to find out the relationships among communication competency, social network centrality, discussion performance, and online boarding activity, compared t-test and path analysis are used. Followings are the results of the research: (a) Communication competency is improved significantly after team based learning. (b) Trust centrality effects significantly on the knowledge sharing centrality. (c) Knowledge sharing effects significantly on discussion performance. (d) Trust centrality effects on the online boarding activity in the team based learning.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.119-128
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• 2009

Many researchers have argued about the relationship between information structure and intonation. Their results can be summarized in three main points: the intonation of topic and focus in English information structures is implemented as i) a pitch accent, ii) a tune (a pitch accent + (an) edge tone(s)), or iii) a boundary tone. The purpose of this paper is to study various intonational patterns of topic and focus in English information structures, using natural conversations. In this paper, the types of topics and foci are divided, based on contrastiveness. The topics are classified as five non-contrastive and four contrastive topics. The foci are classified as neutral focus, informational focus, and contrastive focus. The results show that the intonation of the topic in English information structures is mainly implemented as a pitch accent, except for the type of the pronoun topic (Lp) which is not implemented as a pitch accent or a tune. However, the intonation of the focus is implemented as a tune in the neutral focus (Fn) and as a pitch accent or a tune in the informational focus (Fi) and the contrastive focus (Fe). In our discussion and conclusion, we suggest that it is not always true that for the meaning of contrast, the topic or the focus is represented as a $L+H^{\ast}$ pitch accent, which has been the main contrastive intonation from earlier studies.

• Educational Technology International
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• v.25 no.1
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• pp.1-25
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• 2024

This study aims to analyze the impact of learners' usability perceptions of topic modeling-based visual feedback and generative AI interpretation on reflection levels in online discussions. To achieve this, we asked 17 students in the Department of Korean language education to conduct an online discussion. Text data generated from online discussions were analyzed using LDA topic modeling to extract five clusters of related words, or topics. These topics were then visualized in a network format, and interpretive feedback was constructed through generative AI. The feedback was presented on a website and rated highly for usability, with learners valuing its information usefulness. Furthermore, an analysis using the non-parametric Mann-Whitney U test based on levels of usability perception revealed that the group with higher perceived usability demonstrated higher levels of reflection. This suggests that well-designed and user-friendly visual feedback can significantly promote deeper reflection and engagement in online discussions. The integration of topic modeling and generative AI can enhance visual feedback in online discussions, reinforcing the efficacy of such feedback in learning. The research highlights the educational significance of these design strategies and clears a path for innovation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.35-41
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• 1996

This paper presents a short introduction to fuzzy measures and fuzzy integrals for providing an useful understanding of articles related on fuzzy measure theory and its applications. A brief overview of the basic concepts of systems, models, uncertainty, fuzzy measures and fuzzy integrals is provided. And terminology and notation frequently used in the discussion on the topic are introduced.

• Journal of The Korean Association For Science Education
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• v.24 no.3
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• pp.638-645
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• 2004

In this study, the on-line discussion learning system of physics investigation was developed for developing the creativity in the problem solving and critical thinking. And with the number of participants of a topic unit, the formation of multiple discussion field and a turn-taking, we found that the interaction patterns of the students were composed of interpersonal interaction pattern, interaction pattern of one to one participation, interaction pattern of one to many participation and interaction pattern of many to many participation. These interaction patterns could make us understand the participation structure and the aspect of interaction of the students in the cyber space.