| 1. |
- Kucher, Kostiantyn, 1989-
(författare)
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Sentiment and Stance Visualization of Textual Data for Social Media
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media platforms typically rely on textual data produced or shared by the users in multiple timestamped posts. Analyses of such data are challenging for traditional manual methods that are unable to scale up to the volume and the variety of the data. While computational methods can partially address these challenges, they have to be used together with the methods developed within information visualization and visual analytics to gain knowledge from the text data by using interactive visual representations.One of the most interesting aspects of text data is related to expressions of sentiments and opinions. The corresponding task of sentiment analysis has been studied within computational linguistics, and sentiment visualization techniques exist as well. However, there are gaps in research on the related task of stance analysis, dedicated to subjectivity that is not expressible only in terms of sentiment. Research on stance is an area of interest in linguistics, but support by computational and visual methods has been limited so far. The challenges related to definition, analysis, and visualization of stance in textual data call for an interdisciplinary research effort. The StaViCTA project addressed these challenges with a focus on written text in English. The corresponding results in the area of visualization are reported in this work, based on multiple publications.The main goal of this dissertation is to define, categorize, and implement means for visual analysis of sentiment and stance in textual data, in particular, for social media. Our work is based on the theoretical framework and automatic classifier of stance developed by our project collaborators, involving multiple non-exclusive stance categories such as certainty and prediction. We define a design space for sentiment and stance visualization techniques based on literature surveys. We discuss multiple visualization and visual analytics approaches developed by us to facilitate the underlying research on stance analysis, data collection and annotation, and visual analysis of sentiment and stance in real-world text data from several social media sources. The work described in this dissertation was carried out in cooperation with domain experts in linguistics and computational linguistics, and our approaches were validated with case studies, expert user reviews, and critical discussion. The results of this work open up further opportunities for research in text visualization and visual text analytics. The potential application areas are academic research, business intelligence, social media monitoring, and journalism.
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| 2. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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A survey of surveys on the use of visualization for interpreting machine learning models
- 2020
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 19:3, s. 207-233
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Tidskriftsartikel (refereegranskat)abstract
- Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.
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| 3. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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DeforestVis : Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
- 2024
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659.
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Tidskriftsartikel (refereegranskat)abstract
- As the complexity of Machine Learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.
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| 4. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Empirical Study : Visual Analytics for Comparing Stacking to Blending Ensemble Learning
- 2021
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Ingår i: Proceedings of the 23rd International Conference on Control Systems and Computer Science (CSCS23), 26–28 May 2021, Bucharest, Romania. - : IEEE. - 9781665439404 - 9781665439398 ; , s. 1-8
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel—this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking.
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| 5. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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FeatureEnVi : Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
- 2022
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 28:4, s. 1773-1791
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Tidskriftsartikel (refereegranskat)abstract
- The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
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| 6. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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StackGenVis : Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
- 2021
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE Computer Society Digital Library. - 1077-2626 .- 1941-0506. ; 27:2, s. 1547-1557
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Tidskriftsartikel (refereegranskat)abstract
- In machine learning (ML), ensemble methods—such as bagging, boosting, and stacking—are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.
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| 7. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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t-viSNE : A Visual Inspector for the Exploration of t-SNE
- 2018
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Ingår i: Presented at IEEE Information Visualization (VIS '18), Berlin, Germany, 21-26 October, 2018.
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Konferensbidrag (refereegranskat)abstract
- The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.
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| 8. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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t-viSNE : Interactive Assessment and Interpretation of t-SNE Projections
- 2020
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 26:8, s. 2696-2714
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Tidskriftsartikel (refereegranskat)abstract
- t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool’s effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.
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| 9. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
- 2020
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 39:3, s. 713-756
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Tidskriftsartikel (refereegranskat)abstract
- Machine 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.
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| 10. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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VisEvol : Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
- 2021
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 40:3, s. 201-214
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Tidskriftsartikel (refereegranskat)abstract
- During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
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| 11. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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VisRuler : Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
- 2023
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 22:2, s. 115-139
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Tidskriftsartikel (refereegranskat)abstract
- Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.
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| 12. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Visualization for Trust in Machine Learning Revisited : The State of the Field in 2023
- 2024
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Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 44:3, s. 99-113
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Tidskriftsartikel (refereegranskat)abstract
- Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance.
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| 13. |
- Espadoto, Mateus, et al.
(författare)
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Toward a Quantitative Survey of Dimension Reduction Techniques
- 2021
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 27:3, s. 2153-2173
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Tidskriftsartikel (refereegranskat)abstract
- Dimensionality reduction methods, also known as projections, are frequently used in multidimeDimensionality reduction methods, also known as projections, are frequently used in multidimensional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.nsional data exploration in machine learning, data science, and information visualization. Tens of such techniques have been proposed, aiming to address a wide set of requirements, such as ability to show the high-dimensional data structure, distance or neighborhood preservation, computational scalability, stability to data noise and/or outliers, and practical ease of use. However, it is far from clear for practitioners how to choose the best technique for a given use context. We present a survey of a wide body of projection techniques that helps answering this question. For this, we characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics. We sample these three spaces according to these metrics, aiming at good coverage with bounded effort. We describe our measurements and outline observed dependencies of the measured variables. Based on these results, we draw several conclusions that help comparing projection techniques, explain their results for different types of data, and ultimately help practitioners when choosing a projection for a given context. Our methodology, datasets, projection implementations, metrics, visualizations, and results are publicly open, so interested stakeholders can examine and/or extend this benchmark.
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| 14. |
- Huang, Zeyang, 1998-, et al.
(författare)
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Towards a Visual Analytics System for Emotion Trajectories in Multiparty Conversations
- 2024
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Ingår i: Poster Proceedings of the 26th Eurographics Conference on Visualization (EuroVis 2024 Posters). - : Eurographics - European Association for Computer Graphics. - 9783038682585
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Konferensbidrag (refereegranskat)abstract
- Visualizing 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.
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| 15. |
- Huang, Zeyang, 1998-, et al.
(författare)
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VA + Embeddings STAR : A State-of-the-Art Report on the Use of Embeddings in Visual Analytics
- 2023
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 42:3, s. 539-571
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Tidskriftsartikel (refereegranskat)abstract
- Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.
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| 16. |
- Kucher, Kostiantyn, et al.
(författare)
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Analysis of VINCI 2009–2017 Proceedings
- 2018
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Ingår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 97-101
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Konferensbidrag (refereegranskat)abstract
- Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.
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| 17. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
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Project in Visualization and Data Analysis : Experiences in Designing and Coordinating the Course
- 2021
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Ingår i: Proceedings of 42nd Annual Conference of the European Association for Computer Graphics (EG '21) — Education Papers. - : Eurographics - European Association for Computer Graphics. - 9783038681328 ; , s. 39-44
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Konferensbidrag (refereegranskat)abstract
- Visual analytics involves both visual and computational components for empowering human analysts who face the challenges of making sense and making use of large and heterogeneous data sets in various application domains. In order to facilitate the learning process for the students at higher education institutions with regard to both the theoretical knowledge and practical skills in visual analytics, the respective courses must cover a variety of topics and include multiple assessment methods and activities. In this paper, we report on the design and first instantiation of a full term project-based course in visualization and data analysis, which was recently offered to graduate and post-graduate students at our department and met with positive feedback from the course participants.
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| 18. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
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StanceVis Prime : Visual Analysis of Sentiment and Stance in Social Media Texts
- 2020
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Ingår i: Journal of Visualization. - : Springer. - 1343-8875 .- 1875-8975. ; 23:6, s. 1015-1034
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Tidskriftsartikel (refereegranskat)abstract
- Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest for this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by existing approaches. The challenges associated with this problem include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.
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| 19. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Supporting University Research and Administration via Interactive Visual Exploration of Bibliographic Data
- 2023
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Ingår i: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '23). - : SciTePress. - 9789897586347 ; , s. 248-255
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Konferensbidrag (refereegranskat)abstract
- Bibliographic data and bibliometric analyses play an important role in the professional life of academic researchers, and the quality of the respective publication records is essential for establishing the big picture of the relationships between particular publications, their authors and affiliations, or further data facets associated with publications. In this paper, we report on the design and outcomes of an interactive visual data exploration project conducted within the scope of a university with the goal of gaining overview of the university publication data. The project has been carried out by information visualization researchers in collaboration with several groups of stakeholders, including the university library and administration staff. We describe the design considerations, the resulting interactive visual interface, and the feedback received from the stakeholders with respect to the tool functionality and the insights discovered in the bibliographic data.
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| 20. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Visual Analysis of Humor Assessment Annotations for News Headlines in the Humicroedit Data Set
- 2024
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Ingår i: Proceedings of the First Workshop on Visualization for Natural Language Processing (Vis4NLP 2024). - : Eurographics - European Association for Computer Graphics.
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Konferensbidrag (refereegranskat)abstract
- Effective 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.
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| 21. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Visualization of Swedish News Articles: A Design Study
- 2024
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Ingår i: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '24). - : SciTePress. ; , s. 670-677
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Konferensbidrag (refereegranskat)abstract
- The amount of available text data has increased rapidly in the past years, making it difficult for many users to find relevant information. To solve this, natural language processing (NLP) and text visualization methods have been developed, however, they typically focus on English texts only, while the support for low-resource languages is limited. The aim of this design study was to implement a visualization prototype for exploring a large number of Swedish news articles (made available by industrial collaborators), including the temporal and relational data aspects. Sketches of three visual representations were designed and evaluated through user tests involving both our collaborators and end-users (journalists). Next, an NLP pipeline was designed in order to support dynamic and hierarchical topic modeling. The final part of the study resulted in an interactive visualization prototype that uses a variation of area charts to represent topic evolution. The prototype was evaluated thr ough an internal case study and user tests with two groups of participants with the background in journalism and NLP. The evaluation results reveal the participants’ preference for the representation focusing on top topics rather than the topic hierarchy, while suggestions for future work relevant for Swedish text data visualization are also provided.
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| 22. |
- Martins, Rafael Messias, Dr. 1984-, et al.
(författare)
-
Efficient Dynamic Time Warping for Big Data Streams
- 2019
-
Ingår i: Proceedings of the IEEE International Conference on Big Data (Big Data '18). - : IEEE. - 9781538650356 - 9781538650363 ; , s. 2924-2929
-
Konferensbidrag (refereegranskat)abstract
- Many common data analysis and machine learning algorithms for time series, such as classification, clustering, or dimensionality reduction, require a distance measurement between pairs of time series in order to determine their similarity. A variety of measures can be found in the literature, each with their own strengths and weaknesses, but the Dynamic Time Warping (DTW) distance measure has occupied an important place since its early applications for the analysis and recognition of spoken word. The main disadvantage of the DTW algorithm is, however, its quadratic time and space complexity, which limits its practical use to relatively small time series. This issue is even more problematic when dealing with streaming time series that are continuously updated, since the analysis must be re-executed regularly and with strict running time constraints. In this paper, we describe enhancements to the DTW algorithm that allow it to be used efficiently in a streaming scenario by supporting an append operation for new time steps with a linear complexity when an exact, error-free DTW is needed, and even better performance when either a Sakoe-Chiba band is used, or when a sliding window is the desired range for the data. Our experiments with one synthetic and four natural data sets have shown that it outperforms other DTW implementations and the potential errors are, in general, much lower than another state-of-the-art approximated DTW technique.
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| 23. |
- Neves, Tácito Trindade de Araújo Tiburtino, et al.
(författare)
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Fast and Reliable Incremental Dimensionality Reduction for Streaming Data
- 2022
-
Ingår i: Computers & graphics. - : Elsevier. - 0097-8493 .- 1873-7684. ; 102, s. 233-244
-
Tidskriftsartikel (refereegranskat)abstract
- Streaming data applications are becoming more common due to the ability ofdifferent information sources to continuously capture or produce data, such as sensors and social media. Although there are recent advances, most visualization approaches, particularly Dimensionality Reduction (DR) techniques, cannot be directly applied in such scenarios due to the transient nature of streaming data. A few DR methods currently address this limitation using online or incremental strategies, continuously updating the visualization as data is received. Despite their relative success, most impose the need to store and access the data multiple times to produce a complete projection, not being appropriate for streaming where data continuously grow. Others do not impose such requirements but cannot update the position of the data already projected, potentially resulting in visual artifacts. This paper presents Xtreaming, a novel incremental DR technique that continuously updates the visual representation to reflect new emerging structures or patterns without visiting the high-dimensional data more than once. Our tests show that in streaming scenarios where data is not fully stored in-memory, Xtreaming is competitive in terms of quality compared to other streaming and incremental techniques while being orders of magnitude faster.
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| 24. |
- Skeppstedt, Maria, Dr. 1977-, et al.
(författare)
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Visualising and Evaluating the Effects of Combining Active Learning with Word Embedding Features
- 2019
-
Ingår i: Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019). - : German Society for Computational Linguistics and Language Technology (GSCL). ; , s. 91-100
-
Konferensbidrag (refereegranskat)abstract
- A tool that enables the use of active learning, as well as the incorporation of word embeddings, was evaluated for its ability to decrease the training data set size required for a named entity recognition model. Uncertainty-based active learning and the use of word embeddings led to very large performance improvements on small data sets for the entity categories PERSON and LOCATION. In contrast, the embedding features used were shown to be unsuitable for detecting entities belonging to the ORGANISATION category. The tool was also extended with functionality for visualising the usefulness of the active learning process and of the word embeddings used. The visualisations provided were able to indicate the performance differences between the entities, as well as differences with regards to usefulness of the embedding features.
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| 25. |
- Ulan, Maria, et al.
(författare)
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Artifact: Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities
- 2018
-
Annan publikation (mjukvara/multimedium) (refereegranskat)abstract
- Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.
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| 26. |
- Ulan, Maria, et al.
(författare)
-
Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities
- 2018
-
Ingår i: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018. - : IEEE. - 9781538682920 - 9781538682937 ; , s. 65-75
-
Konferensbidrag (refereegranskat)abstract
- Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.
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| 27. |
- Wang, Jinyi, 1997-, et al.
(författare)
-
Visual Analysis of Power Plant Data for European Countries
- 2024
-
Ingår i: Poster Proceedings of the 26th Eurographics Conference on Visualization (EuroVis 2024 Posters). - : Eurographics - European Association for Computer Graphics. - 9783038682585
-
Konferensbidrag (refereegranskat)abstract
- A 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.
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| 28. |
- Witschard, Daniel, et al.
(författare)
-
A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks
- 2021
-
Ingår i: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP. - : SciTePress. - 9789897584886 ; , s. 219-223
-
Konferensbidrag (refereegranskat)abstract
- The visualization of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. This position paper contrasts the current trend of finding new ways of jointly embedding several properties with the alternative strategy of instead using, and combining, already existing state-of-the-art single scope embedding technologies. From this comparison, we argue that the latter strategy provides a more generic and flexible approach with several advantages. Hence, we hope to convince the visual analytics community to invest more work in resolving some of the key issues that would make this methodology possible.
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| 29. |
- Witschard, Daniel, et al.
(författare)
-
Interactive Optimization of Embedding-based Text Similarity Calculations
- 2022
-
Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 21:4, s. 335-353
-
Tidskriftsartikel (refereegranskat)abstract
- Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.
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| 30. |
- Witschard, Daniel, et al.
(författare)
-
Multiple Embeddings for Multivariate Network Analysis
- 2020
-
Ingår i: 6th annual Big Data Conference at Linnaeus University, in Växjö, Sweden.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The visualization and visual analytics of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Embeddings are relatively low-dimensional vector representations of the embedded items and they are well suited for similarity calculations. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. As a possible way forward we suggest a new angle of approach where, instead of trying to fit all aspects of a MVN into one embedding, the strategy would be to embed each property by itself and then find ways to combine these sets of embeddings.
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| 31. |
- Witschard, Daniel, et al.
(författare)
-
Visually Guided Network Reconstruction Using Multiple Embeddings
- 2023
-
Ingår i: Proceedings of the 16th IEEE Pacific Visualization Symposium (PacificVis '23), visualization notes track, IEEE, 2023. - : IEEE. - 9798350321241 - 9798350321258 ; , s. 212-216
-
Konferensbidrag (refereegranskat)abstract
- Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this paper, we extend our previous work on using multiple embeddings for text similarity calculations to the field of networks. The embedding ensemble approach improves network reconstruction performance compared to single-embedding strategies. Our visual analytics methodology is successful in handling both text and network data, which demonstrates its generalizability beyond its originally presented scope.
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| 32. |
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| 33. |
- Büschel, Wolfgang, et al.
(författare)
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Interaction for Immersive Analytics
- 2018
-
Ingår i: Immersive Analytics. - Cham : Springer. - 9783030013875 - 9783030013882 ; , s. 95-138
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions.
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| 34. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Analyzing the Evolution of JavaScript Applications
- 2019
-
Ingår i: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE. - : SciTePress. - 9789897583759 ; , s. 359-366
-
Konferensbidrag (refereegranskat)abstract
- Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.
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| 35. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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HardVis : Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
- 2023
-
Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 42:1, s. 135-154
-
Tidskriftsartikel (refereegranskat)abstract
- Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model’s predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.
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| 36. |
- Collins, Christopher, et al.
(författare)
-
Visual Text Analytics : Report from Dagstuhl Seminar 22191
- 2022
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Text data is one of the most abundant types of data available, produced every day across all domains of society. Understanding the contents of this data can support important policy decisions, help us understand society and culture, and improve business processes. While machine learning techniques are growing in their power for analyzing text data, there is still a clear role for human analysis and decision-making. This seminar explored the use of visual analytics applied to text data as a means to bridge the complementary strengths of people and computers. The field of visual text analytics applies visualization and interaction approaches which are tightly coupled to natural language processing systems to create analysis processes and systems for examining text and multimedia data. During the seminar, interdisciplinary working groups of experts from visualization, natural language processing, and machine learning examined seven topic areas to reflect on the state of the field, identify gaps in knowledge, and create an agenda for future cross-disciplinary research. This report documents the program and the outcomes of Dagstuhl Seminar 22191 "Visual Text Analytics".
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| 37. |
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| 38. |
-
Computer Vision, Imaging and Computer Graphics Theory and Applications : 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers
- 2020
-
Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, held in Prague, Czech Republic, in February 2019. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 395 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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| 39. |
-
Computer Vision, Imaging and Computer Graphics Theory and Applications : 15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27–29, 2020, Revised Selected Papers
- 2022. - 1
-
Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, held in Valletta, Malta, in February 2020. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 455 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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| 40. |
- Conroy, Melanie, et al.
(författare)
-
Uncertainty in humanities network visualization
- 2024
-
Ingår i: Frontiers in Communication. - : Frontiers Media S.A.. - 2297-900X. ; 8
-
Tidskriftsartikel (refereegranskat)abstract
- Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated into the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.
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| 41. |
- Feyer, Stefan P., et al.
(författare)
-
2D, 2.5D, or 3D? : An Exploratory Study on Multilayer Network Visualisations in Virtual Reality
- 2024
-
Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 30:1, s. 469-479
-
Tidskriftsartikel (refereegranskat)abstract
- Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.
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| 42. |
-
Graph Drawing and Network Visualization : 26th International Symposium, GD 2018, Barcelona, Spain, September 26-28, 2018, Proceedings
- 2018
-
Proceedings (redaktörskap) (refereegranskat)abstract
- This book constitutes the refereed proceedings of the 26th International Symposium on Graph Drawing and Network Visualization, GD 2018, held in Barcelona, Spain, in September 2018. The 41 full papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named: planarity variants; upward drawings; RAC drawings; orders; crossings; crossing angles; contact representations; specialized graphs and trees; partially fixed drawings, experiments; orthogonal drawings; realizability; and miscellaneous. The book also contains one invited talk in full paper length and the Graph Drawing contest report.
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| 43. |
- Huang, Zeyang, 1998-, et al.
(författare)
-
Matrix Snap&Go: Visualization of Paths on Matrices
- 2024
-
Konferensbidrag (refereegranskat)abstract
- Matrix representations can be effective for visualizing networks. However, it is very difficult to follow or explore specific paths in a matrix representation. In this paper, we introduce an interactive method for exploring paths on a matrix, called Matrix Snap&Go. Our visualization approach relies heavily on interactive exploration, bringing in the local neighborhood of selected nodes and tracing the path progression through the matrix. We demonstrate the utility of our approach by performing and analyzing test runs with synthetic input graphs of various node/edge densities as well as by discussing a use case based on the exploration of citation networks.
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| 44. |
- Huang, Zeyang, 1998-, et al.
(författare)
-
Towards an Exploratory Visual Analytics System for Multivariate Subnetworks in Social Media Analysis
- 2022
-
Ingår i: Poster Abstracts, IEEE Visualization and Visual Analytics (VIS '22). - : IEEE.
-
Konferensbidrag (refereegranskat)abstract
- Identifying sociolinguistic attributes of inter-community interactions is essential for understanding the polarization of social network communities. A wide range of computational text and network analysis methods may be applicable for this task, however, interpretation of the respective results and investigation of particularly interesting cases and subnetworks are difficult due to the scale and complexity of the data, e.g., for the Reddit platform. In this poster paper, we present an interactive visual analysis interface that facilitates network exploration and comparison at different topological and multivariate attribute scales. Users are able to investigate text- and network-based properties of social network community interactions, identify anomalies of conflict starters, or gain insight into multivariate anomalies behind groups of negative social media posts.
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| 45. |
- Kerren, Andreas, Dr.-Ing. 1971-
(författare)
-
Special issue on VINCI 2016 best papers
- 2018
-
Ingår i: Journal of Visual Languages and Computing. - : Elsevier. - 1045-926X .- 1095-8533. ; 48, s. 9-9
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 46. |
- Kerren, Andreas, Dr.-Ing. 1971-, et al.
(författare)
-
Visualisering och bildbehandling
- 2021. - 1
-
Ingår i: Medicinisk Informatik. - : Liber. - 9789147134083 ; , s. 95-110
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 47. |
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| 48. |
- Kucher, Kostiantyn, et al.
(författare)
-
Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
- 2018
-
Ingår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18). - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 102-103
-
Konferensbidrag (refereegranskat)abstract
- The analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, stances, arguments, topics, terms, and text documents are usually connected with many-to-many relationships for such tasks. Exploratory visual analysis with interactive tools can help the analysts to get an overview of the topics and opinions, identify particularly interesting documents, and describe main themes of various arguments. In our previous work, we introduced an interactive tool called Topics2Themes that was used for topic and theme analysis of vaccination-related discussion texts with a limited set of stance categories. In this poster paper, we describe an application of Topics2Themes to a different genre of data, namely, political comments from Reddit, and multiple sentiment and stance categories detected with automatic classifiers.
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| 49. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
-
Task-Based Evaluation of Sentiment Visualization Techniques
- 2022
-
Ingår i: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '22). - : SciTePress. - 9789897585555 ; , s. 187-194
-
Konferensbidrag (refereegranskat)abstract
- Sentiment visualization is concerned with visual representation of sentiments, emotions, opinions, and stances typically detected in textual data, for example, charts or diagrams representing negative and positive opinions in online customer reviews or Twitter discussions. Such approaches have been applied for the purposes of academic research and practical applications in the past years. But the question of usability of these various techniques still remains generally unsolved, as the existing research typically addresses individual design alternatives for a particular technique implementation only. This work focuses on evaluation of the effectiveness and efficiency of common visual representations for low-level visualization tasks in the context of sentiment visualization. More specifically, we describe a task-based within-subject user study for various tasks, carried out as an online survey and taking the task completion time and error rate into account for most questions. The study involved 50 participants, and we present and discuss their responses and free-form comments. The results provide evidence of strengths and weaknesses of particular representations and visual variables with respect to different tasks, as well as specific user preferences, in the context of sentiment visualization.
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| 50. |
- Kucher, Kostiantyn, et al.
(författare)
-
Text Visualization Revisited : The State of the Field in 2019
- 2019
-
Ingår i: Posters of the 21th EG/VGTC Conference on Visualization (EuroVis '19). - : Eurographics - European Association for Computer Graphics. - 9783038680888 ; , s. 29-31
-
Konferensbidrag (refereegranskat)abstract
- Text and document data visualization is an important research field within information visualization and visual analytics with multiple application domains including digital humanities and social media, for instance. During the past five years, we have been collecting text visualization techniques described in peer-reviewed literature, categorizing them according to a detailed categorization schema, and providing the resulting manually curated collection in an online survey browser. In this poster paper, we present the updated results of analyses of this data set as of spring 2019. Compared to the recent surveys and meta-analyses that mainly focus on particular aspects and problems related to text visualization, our results provide an overview of the current state of the text visualization field and the respective research community in general.
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