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Item The State of the Art in Sentiment Visualization(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Kucher, Kostiantyn; Paradis, Carita; Kerren, Andreas; Chen, Min and Benes, BedrichVisualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization.Item The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations(The Eurographics Association and John Wiley & Sons Ltd., 2020) Chatzimparmpas, Angelos; Martins, Rafael M.; Jusufi, Ilir; Kucher, Kostiantyn; Rossi, Fabrice; Kerren, Andreas; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, BeiMachine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.Item Towards a Visual Analytics System for Emotion Trajectories in Multiparty Conversations(The Eurographics Association, 2024) Huang, Zeyang; Kucher, Kostiantyn; Kerren, Andreas; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, ChristinaVisualizing sentiments in textual data has received growing interest; however, representing emotions within interlocutor relationships and associating them with the temporal progression of dialogues remains challenging. In this poster abstract, we describe the ongoing work on a visual analytics tool designed for analyzing emotion trajectories within dialogue collections composed of utterances from multiple speakers. The proposed tool provides exploration at different levels of detail to complex multigraphs, where edges represent direct responses between speakers through their utterances. Our approach includes several selection strategies for connecting different views: summaries of emotion transitions across dialogue groups, detailed analyses of individual utterances within specific dialogues of interest in interlocutor networks, and close reading. The tool aims to support model development in natural language processing by allowing users to explore text corpora interactively.Item Visual Analysis of Humor Assessment Annotations for News Headlines in the Humicroedit Data Set(The Eurographics Association, 2024) Kucher, Kostiantyn; Akkurt, Elin; Folde, Johanna; Kerren, Andreas; Yousef, Tariq; Al-Khatib, KhalidEffective utilization of training data is a critical component for the success of any artificial intelligence algorithm, including natural language processing (NLP) tasks. One particular task of interest is related to detecting or ranking humor in texts, as exemplified by the Humicroedit data set used for the SemEval 2020 task of assessing humor in micro-edited news headlines. Rather than focusing on text classification or prediction, in this study, we focus on gaining a deeper understanding and utilization of the data through the use of information visualization techniques facilitated by the established NLP methods such as sentiment analysis and topic modeling. We describe the design of an interactive visualization tool prototype that relies on multiple coordinated views to allow the user explore and analyze the relationships between the annotated humor scores, sentiments, and topics. Evaluation of the proposed approach involves a case study with the Humicroedit data set as well as domain expert reviews with four participants. The experts deemed the prototype useful for its purpose and saw potential in exploring similar data sets with it, as well as further potential applications in their line of work. Our study thus contributes to the body of work on visual text analytics for supporting computational humor analysis as well as annotated text data analysis in general.Item Visual Analysis of Power Plant Data for European Countries(The Eurographics Association, 2024) Wang, Jinyi; Kucher, Kostiantyn; Kerren, Andreas; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, ChristinaA power plant is a complex real-world system associated with rich multidimensional data relevant to its construction and activity. Thus, choosing an appropriate way to visualize power plant data is important for users to understand and explore more about such systems. Most of the approaches existing in this field support only a static representation of data from a small region. This makes it hard for the users to get an overview or explore specific power plants. In this poster abstract, we describe an interactive visualization tool designed for the analysis of power plant data in Europe. Our approach provides an overview and detail visualization approach for Global Power Plant Database entries. With this tool, users can easily find power plants, see details on demand, filter, compare, and explore the power plant outage scenarios from the nearest neighbor perspective.Item EuroVis 2020 Short Papers: Frontmatter(The Eurographics Association, 2020) Kerren, Andreas; Garth, Christoph; Marai, G. Elisabeta; Kerren, Andreas and Garth, Christoph and Marai, G. ElisabetaItem MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks(The Eurographics Association, 2017) Martins, Rafael M.; Kruiger, J. F.; Minghim, Rosane; Telea, Alexandru C.; Kerren, Andreas; Barbora Kozlikova and Tobias Schreck and Thomas WischgollThe analysis of Multivariate Networks (MVNs) can be approached from two different perspectives: a multidimensional one, consisting of the nodes and their multiple attributes, or a relational one, consisting of the network's topology of edges. In order to be comprehensive, a visual representation of an MVN must be able to accommodate both. In this paper, we propose a novel approach for the visualization of MVNs that works by combining these two perspectives into a single unified model, which is used as input to a dimensionality reduction method. The resulting 2D embedding takes into consideration both attribute- and edge-based similarities, with a user-controlled trade-off. We demonstrate our approach by exploring two real-world data sets: a co-authorship network and an open-source software development project. The results point out that our method is able to bring forward features of MVNs that could not be easily perceived from the investigation of the individual perspectives only.Item EuroVis 2024 CGF 43-3 STARs: Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2024) Garth, Christoph; Kerren, Andreas; Raidou, Renata; Garth, Christoph; Kerren, Andreas; Raidou, RenataItem MDS-based Visual Survey of Biological Data Visualization Techniques(The Eurographics Association, 2017) Kerren, Andreas; Kucher, Kostiantyn; Li, Yuan-Fang; Schreiber, Falk; Anna Puig Puig and Tobias IsenbergData visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for biological data visualization have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers as well as visualization researchers. To address this challenge, we have reviewed visualization research for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods.Item Graph Layouts by t-SNE(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kruiger, J. F.; Rauber, Paulo E.; Martins, Rafael Messias; Kerren, Andreas; Kobourov, Stephen; Telea, Alexandru C.; Heer, Jeffrey and Ropinski, Timo and van Wijk, JarkeWe propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.
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