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About 6,459 papers for “topic visualization”
Zhijun Yin (5032604), Lijun Song (1899229), Ellen W. Clayton (5916215) et al. · Figshare · 2020
<p>The topic index in each circle is corresponding to the presenting order of the topics in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0238644#pone.0238644.t001" target="_blank">Table 1</a>.</p> <p>(TIF)</p>
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Vicinanza, Paul · Figshare · 2022
Html topic visualization produced via the python package <em>pyLDAvis</em>. <br> Relevant conspiracy topics: <br> 4 - Plandemic 14 - Anti-Fauci/Judy Mikovits 36 - Fake Death Statistics 45 - Government control 52 - 5g 63 - China Created Covid 65 - Globalist control 84 - Generic fake 89 - Empty hosptials 100 - Bill Gates Vaccine 106 - QAnon 130 - False positives 140 - Anti-vaccine
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Vicinanza, Paul · Figshare · 2022
Html topic visualization produced via the python package <em>pyLDAvis</em>. <br> Relevant conspiracy topics: <br> 4 - Plandemic 14 - Anti-Fauci/Judy Mikovits 36 - Fake Death Statistics 45 - Government control 52 - 5g 63 - China Created Covid 65 - Globalist control 84 - Generic fake 89 - Empty hosptials 100 - Bill Gates Vaccine 106 - QAnon 130 - False positives 140 - Anti-vaccine
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Bénédicte Le Grand · 2007
Introduction Topic maps provide a bridge between the domains of knowledge representation and information management. Topics and associations build a semantic network abov e information resources, which allows users to navigate at a higher level of abstraction. However, this semantic network may contain millions of topics and associations, and users may still have problems to find relevant information; therefore, the issue of topic maps visualization and navigation is essential. A topic map defines a multidimensional topic space: a topic has one or more names within a scope; it can also have occurrences and may play a role as a member of zero or more associations. Topics, a ssociations and occurrence may have a type which is a topic, etc. A topic map is actually a high-dimensional knowledge base. This chapter is organized as follows: first we will discuss topic maps visualization requirements. Then, we will review existing h igh-dimensional visualization techniques and study how/if the
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Schliemann, André Lisandro · OSF Preprints (OSF Preprints) · 2025
This interactive visualization was created using pyLDAVis to explore the results of Latent Dirichlet Allocation (LDA) modeling applied to a corpus of academic literature on motor behavior in autism.
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Sandeep Reddivari, W HACKNEY · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2018
Latent Direchlet Allocation (LDA) is a statistical topic modeling approach that has been used to support several software engineering activities. The main assumption is that LDA offers a unique insight into the semantic content of software systems, thus revealing otherwise unseen relations between software artifacts. However, a main problem when dealing with LDA is the complexity of its output. In particular, the numerical probabilistic distributions produced by LDA to represent topics and documents are not intuitive to understand and rationalize. To address this problem, in this paper we present a topic modeling based approach to visualize software systems based on LDA. We also present several visualizations to represent the basic elements of LDA including words, topics, and documents. These different basic views are combined through a set of integration links to enable users to effectively explore software systems by supporting knowledge discovery at different levels of abstraction. We also demonstrate how the topic modeling based visualization approach can provide support to several software engineering activities such as program comprehension, software clustering, and code evolution analysis.
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Bénédicte Le Grand, Michel Soto · 2000
Topic maps - the new ISO standard ISO-IEC 13250 - provide a bridge between the domains of knowledge representation and information management. Topics and topic associations build a structured semantic link network above information resources. Our research aims at visualizing this semantic layer efficiently, which is a critical issue as topic maps may contain millions of topics and associations. This paper is divided into 3 parts. First, we depict briefly basic topic maps concepts. Then, we review a few graph visualization techniques. Finally, we describe the visualization tool we developed at the Laboratoire d&apos;Informatique de Paris 6 and study how this tool may be used - and enhanced - for topic maps visualization.
Cited by 34
Yeseul Jeon, Jina Park, Ick Hoon Jin et al. · Journal of Applied Statistics · 2024
In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we utilize the topic-words distribution, obtained from topic models, as item-response data to model the structure of topics using a latent space item response model. By estimating the latent positions of topics based on their distances toward words, we can capture the underlying topic structure and reveal their relationships. Visualizing the latent positions of topics in Euclidean space allows for an intuitive understanding of their proximity and associations. We interpret relationships among topics by characterizing each topic based on representative words selected using a newly proposed scoring scheme. Additionally, we assess the maturity of topics by tracking their latent positions using different word sets, providing insights into the robustness of topics. To demonstrate the effectiveness of our approach, we analyze the topic composition of COVID-19 studies during the early stage of its emergence using biomedical literature in the PubMed database. The software and data used in this paper are publicly available at https://github.com/jeon9677/gViz.
Cited by 1Open access
Jeon, Yeseul, Park, Jina, Jin, Ick Hoon et al. · Figshare · 2024
In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we utilize the topic-words distribution, obtained from topic models, as item-response data to model the structure of topics using a latent space item response model. By estimating the latent positions of topics based on their distances toward words, we can capture the underlying topic structure and reveal their relationships. Visualizing the latent positions of topics in Euclidean space allows for an intuitive understanding of their proximity and associations. We interpret relationships among topics by characterizing each topic based on representative words selected using a newly proposed scoring scheme. Additionally, we assess the maturity of topics by tracking their latent positions using different word sets, providing insights into the robustness of topics. To demonstrate the effectiveness of our approach, we analyze the topic composition of COVID-19 studies during the early stage of its emergence using biomedical literature in the PubMed database. The software and data used in this paper are publicly available at https://github.com/jeon9677/gViz.
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Valerie Müller, Christian Sieg, Lars Linsen · arXiv (Cornell University) · 2021
Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuration as well as a number of parameter values than need to be chosen. This induces uncertainties on the topic modeling results, and visualization methods should convey these uncertainties during the analysis process. We propose a visual uncertainty-aware topic modeling analysis. We capture the uncertainty by computing topic modeling ensembles and propose measures for estimating topic modeling uncertainty from the ensemble. Then, we propose to enhance state-of-the-art topic modeling visualization methods to convey the uncertainty in the topic modeling process. We visualize the entire ensemble of topic modeling results at different levels for topic and document analysis. We apply our visualization methods to a text corpus to document the impact of uncertainty on the analysis.
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Ping Wang · Työväentutkimus Vuosikirja · 2017
Latent semantic structure in a text collection is called a topic. In this thesis, we aim to visualize topics in the scientific literature and detect active or inactive research areas based on their lifetime. Topics were extracted from over 1 million abstracts from the arXiv.org database using Latent Dirichlet Allocation (LDA). Hellinger distance measures similarity between two topics. Topics are determined to be relevant if their pairwise distances are smaller than the threshold of Hellinger distance we set beforehand. The dynamic topic graph displays the evolution of topics over time. Topic hierarchical relationships are shown in a tree, where topics near the leaves are subtopics to those far from the bottom. The dynamic topic graph of category focuses on topics associated with a particular categories in the arXiv classification system.\n\nLogistic regression was used to predict topic lifetime and discover which factors have positive effect on lifetime and which ones induce the death of topics. Especially, we are interested in the effect of time, category, the number of documents, the number of topic variety and their interactions. In the experiment, we investigated topics in the dynamic topic graph of category under thresholds of Hellinger distance of 0.4, 0.5 and 0.6, respectively. Categories whose coefficients were negative for all datasets are defined to be popular as topics in this field are more probable to survive.
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Luisa Stracqualursi (14135348), Patrizia Agati (14135351) · Figshare · 2022
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Luisa Stracqualursi (14135348), Patrizia Agati (14135351) · Figshare · 2022
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Dang Pham, Tuan M. V. Le · Lecture notes in computer science · 2021
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Luisa Stracqualursi (14135348), Patrizia Agati (14135351) · Figshare · 2022
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Oliveira, Alexandre Rocha, Malgueiro, Octávio, Grave, Luís et al. · RepositóriUM (Universidade do Minho) · 2002
We are living a phenomenon of accelerated information production with different accessing sources. Hence users are faced with a growing problem: accessing (navigation) and filtering specific information contained in large datasets, which are increasing in size. Procedures such as data filtering and gathering are now simplified through a new concept known as Topic Maps. The application of Virtual Reality technologies enables to present and interact with multidimensional information in a 3D space. In this paper we present INSPHERE, a new visual metaphor for information visualization, based on both, Virtual Reality techniques, and “geographical information maps” provided by Topic Maps.
Cited by 1Open access
Pascal Auillans · HAL (Le Centre pour la Communication Scientifique Directe) · 2002
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Carson Sievert, Kenny Shirley · 2015
Tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model.
Cited by 23Open access
Hyeon-Jin Kim (384994), Galip Gurkan Yardımcı (9396425), Giancarlo Bonora (3400169) et al. · Figshare · 2020
<p>The resulting topic-cell is normalized by the total number of topic assignments for each topic and visualized. Columns and rows are hierarchically clustered using Euclidean distance with Ward’s clustering algorithm. Colors bars indicate normalized topic assignment values.</p> <p>(TIF)</p>
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Sana Rabbani (20685187), Fasih Ahmed (12101866) · Figshare · 2025
<div> The present study investigates air pollution dynamics through newspaper discourse in India and Pakistan, where both countries rank among the top five countries affected by air pollution. The study focused on newspaper discourses over almost two decades (2005–2023). The study applied Latent Dirichlet Allocation (LDA), a robust algorithm for analyzing the large text corpus. The study underpinned Computational Grounded Theory, which relies on the fact that computation is a way to reveal the hidden meanings beyond the text. The LDA-generated topics reveal that both countries face the toxicological effects of air pollution on health. The primary topics extracted through LDA revolve around discourses related to vehicular emissions, industrial emissions, and urbanization. In addition, the control measures taken by both countries relate to emission standards. The study also has implications for policymakers and planners considering these directions to control air pollution. </div>
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