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Kucher, K., Martins, R. M. & Kerren, A. (2018). Analysis of VINCI 2009–2017 Proceedings. In: Karsten Klein, Yi-Na Li, and Andreas Kerren (Ed.), Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden: . Paper presented at 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden (pp. 97-101). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Analysis of VINCI 2009–2017 Proceedings
2018 (English)In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, p. 97-101Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018
Keywords
meta-analysis, survey, overview, visualization, scientific literature, topic modeling
National Category
Computer Sciences Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-75857 (URN)10.1145/3231622.3231641 (DOI)978-1-4503-6501-7 (ISBN)
Conference
11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden
Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2018-09-11Bibliographically approved
Kucher, K., Skeppstedt, M. & Kerren, A. (2018). Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts. In: Karsten Klein, Yi-Na Li, and Andreas Kerren (Ed.), Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18): . Paper presented at 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden (pp. 102-103). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
2018 (English)In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18) / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, p. 102-103Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018
Keywords
visualization, interaction, topic modeling, argument extraction, text visualization, sentiment analysis, sentiment visualization, stance analysis, stance visualization, annotation
National Category
Computer Sciences Language Technology (Computational Linguistics) Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-75856 (URN)10.1145/3231622.3232505 (DOI)978-1-4503-6501-7 (ISBN)
Conference
11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden
Funder
Swedish Research Council, 2016-06681
Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2018-09-11Bibliographically approved
Kucher, K., Paradis, C. & Kerren, A. (2018). DoSVis: Document Stance Visualization. In: Alexandru C. Telea, Andreas Kerren, and José Braz (Ed.), Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18): . Paper presented at International Conference on Information Visualization Theory and Applications (IVAPP), Funchal-Madeira, Portugal, 27-29 January, 2018 (pp. 168-175). SciTePress, 3
Open this publication in new window or tab >>DoSVis: Document Stance Visualization
2018 (English)In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18) / [ed] Alexandru C. Telea, Andreas Kerren, and José Braz, SciTePress, 2018, Vol. 3, p. 168-175Conference paper, Published paper (Refereed)
Abstract [en]

Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer’s attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature. 

Place, publisher, year, edition, pages
SciTePress, 2018
Keywords
Stance Visualization, Sentiment Visualization, Text Visualization, Stance Analysis, Sentiment Analysis, Text Analytics, Information Visualization, Interaction
National Category
Computer Sciences Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-68428 (URN)10.5220/0006539101680175 (DOI)978-989-758-289-9 (ISBN)
Conference
International Conference on Information Visualization Theory and Applications (IVAPP), Funchal-Madeira, Portugal, 27-29 January, 2018
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2018-04-12Bibliographically approved
Kucher, K., Paradis, C. & Kerren, A. (2018). The State of the Art in Sentiment Visualization. Computer graphics forum (Print), 37(1), 71-96, Article ID CGF13217.
Open this publication in new window or tab >>The State of the Art in Sentiment Visualization
2018 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed) Published
Abstract [en]

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. 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. 

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
sentiment visualization, text visualization, sentiment analysis, opinion mining
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-62644 (URN)10.1111/cgf.13217 (DOI)000426151300007 ()
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2018-03-16Bibliographically approved
Skeppstedt, M., Kucher, K., Stede, M. & Kerren, A. (2018). Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics. In: Mennatallah El-Assady, Annette Hautli-Janisz, and Verena Lyding (Ed.), Proceedings of the LREC 2018 Workshop “The 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III)”: . Paper presented at 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III) at LREC '18, 12 May, 2018, Miyazaki, Japan (pp. 9-16). Paris, France: European Language Resources Association (ELRA)
Open this publication in new window or tab >>Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics
2018 (English)In: Proceedings of the LREC 2018 Workshop “The 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III)” / [ed] Mennatallah El-Assady, Annette Hautli-Janisz, and Verena Lyding, Paris, France: European Language Resources Association (ELRA) , 2018, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

The large collections of opinionated text that are continuously being created online, e.g., in the form of forum posts or tweets, contain arguments that might help us to better understand why opinions are held. While the task of manually extracting arguments from these large collections is an intractable one, a tool for computer-assisted extraction can (i) automatically select a subset of the text collection that contains re-occurring arguments to minimise the amount of text that the human coder has to read, and (ii) present the selected texts in a way that facilitates manual coding of arguments. We propose a tool called Topics2Themes that uses topic modelling to automatically extract important topics as well as the terms and texts most closely associated with each topic. We also provide a graphical user interface for manual argument coding, in which the user can search for arguments in the texts selected, create a theme for each type of argument detected and connect it to the texts in which it is found. Topics, terms, texts and themes are displayed as elements in four separate lists, and associations between the elements are visualised through connecting links. It is also possible to focus on one particular element through the sorting functionality provided, e.g., when a topic is selected, the terms, texts and themes associated with this topic are sorted as the top-ranked elements in their respective lists. The text collection can thereby be explored from different angles, which can be used to facilitate the argument coding and gain an overview and understanding of the arguments found in the texts. 

Place, publisher, year, edition, pages
Paris, France: European Language Resources Association (ELRA), 2018
Keywords
argument extraction, topic modelling, text analysis, argument visualization, stance visualization, text visualization, information visualization, interaction
National Category
Language Technology (Computational Linguistics) Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-70911 (URN)979-10-95546-13-9 (ISBN)
Conference
3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III) at LREC '18, 12 May, 2018, Miyazaki, Japan
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Swedish Research Council, 2016-06681
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-05-24
Kucher, K., Paradis, C. & Kerren, A. (2018). Visual Analysis of Sentiment and Stance in Social Media Texts. In: Anna Puig and Renata Raidou (Ed.), EuroVis 2018 - Posters: . Paper presented at The 20th EG/VGTC Conference on Visualization (EuroVis '18), Brno, Czech Republic, 4-8 June, 2018 (pp. 49-51). Eurographics - European Association for Computer Graphics
Open this publication in new window or tab >>Visual Analysis of Sentiment and Stance in Social Media Texts
2018 (English)In: EuroVis 2018 - Posters / [ed] Anna Puig and Renata Raidou, Eurographics - European Association for Computer Graphics, 2018, p. 49-51Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2018
Keywords
visual analytics, visualization, information visualization, interaction, sentiment analysis, stance analysis, text mining, natural language processing
National Category
Computer Sciences Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-73397 (URN)10.2312/eurp.20181127 (DOI)978-3-03868-065-9 (ISBN)
Conference
The 20th EG/VGTC Conference on Visualization (EuroVis '18), Brno, Czech Republic, 4-8 June, 2018
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2018-09-13Bibliographically approved
Kucher, K., Paradis, C., Sahlgren, M. & Kerren, A. (2017). Active Learning and Visual Analytics for Stance Classification with ALVA. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), Article ID 14.
Open this publication in new window or tab >>Active Learning and Visual Analytics for Stance Classification with ALVA
2017 (English)In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed) Published
Abstract [en]

The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine learning methods create an opportunity to gain insight into the speakers' attitudes towards their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. In order to facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy in order to select suitable candidate utterances for manual annotation. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Publications, 2017
Keywords
visualization, stance visualization, active learning, text visualization, sentiment visualization, annotation, visual analytics, sentiment analysis, stance analysis, NLP, text analytics
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-67173 (URN)10.1145/3132169 (DOI)000414322200005 ()
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-08-05 Created: 2017-08-05 Last updated: 2018-04-04Bibliographically approved
Simaki, V., Paradis, C., Skeppstedt, M., Sahlgren, M., Kucher, K. & Kerren, A. (2017). Annotating speaker stance in discourse: the Brexit Blog Corpus. Corpus linguistics and linguistic theory
Open this publication in new window or tab >>Annotating speaker stance in discourse: the Brexit Blog Corpus
Show others...
2017 (English)In: Corpus linguistics and linguistic theory, ISSN 1613-7027, E-ISSN 1613-7035Article in journal (Refereed) Epub ahead of print
Abstract [en]

The aim of this study is to explore the possibility of identifying speaker stance in discourse, provide an analytical resource for it and an evaluation of the level of agreement across speakers. We also explore to what extent language users agree about what kind of stances are expressed in natural language use or whether their interpretations diverge. In order to perform this task, a comprehensive cognitive-functional framework of ten stance categories was developed based on previous work on speaker stance in the literature. A corpus of opinionated texts was compiled, the Brexit Blog Corpus (BBC). An analytical protocol and interface (ALVA) for the annotations was set up and the data were independently annotated by two annotators. The annotation procedure, the annotation agreements and the co-occurrence of more than one stance in the utterances are described and discussed. The careful, analytical annotation process has returned satisfactory inter- and intra-annotation agreement scores, resulting in a gold standard corpus, the final version of the BBC. 

Keywords
text annotation, blog post texts, modality, evaluation, positioning
National Category
Language Technology (Computational Linguistics) General Language Studies and Linguistics
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-67319 (URN)10.1515/cllt-2016-0060 (DOI)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2018-02-27
Kerren, A., Kucher, K., Li, Y.-F. & Schreiber, F. (2017). BioVis Explorer: A visual guide for biological data visualization techniques. PLoS ONE, 12(11), Article ID e0187341.
Open this publication in new window or tab >>BioVis Explorer: A visual guide for biological data visualization techniques
2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, article id e0187341Article in journal (Refereed) Published
Abstract [en]

Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data 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 and visualization researchers. To address this challenge, we have reviewed visualization research especially performed 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. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.

Keywords
Visualization, Survey, Multidimensional Scaling, Dimensionality Reduction, Biological Data, BioVis
National Category
Computer Sciences Bioinformatics (Computational Biology) Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-68573 (URN)10.1371/journal.pone.0187341 (DOI)000414229700058 ()29091942 (PubMedID)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2018-01-13Bibliographically approved
Skeppstedt, M., Kucher, K., Paradis, C. & Kerren, A. (2017). Language Processing Components of the StaViCTA Project. In: Roussanka Loukanova and Kristina Liefke (Ed.), Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing 2017): . Paper presented at Workshop on Logic and Algorithms in Computational Linguistics (LACompLing '17), 16–19 August 2017, Stockholm, Sweden (pp. 137-138). Stockholm University ; KTH
Open this publication in new window or tab >>Language Processing Components of the StaViCTA Project
2017 (English)In: Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing 2017) / [ed] Roussanka Loukanova and Kristina Liefke, Stockholm University ; KTH , 2017, p. 137-138Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The StaViCTA project is concerned with visualising the expression of stance in written text, and is therefore dependent on components for stance detection. These components are to (i) download and extract text from any HTML page and segment it into sentences, (ii) classify each sentence with respect to twelve different, notionally motivated, stance categories, and (iii) provide a RESTful HTTP API for communication with the visualisation components. The stance categories are certainty, uncertainty, contrast, recommendation, volition, prediction, agreement, disagreement, tact, rudeness, hypotheticality, and source of knowledge. 

Place, publisher, year, edition, pages
Stockholm University ; KTH, 2017
Keywords
Annotation, stance, visualization, visual analytics, NLP, machine learning, classifier, tools
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-66071 (URN)
Conference
Workshop on Logic and Algorithms in Computational Linguistics (LACompLing '17), 16–19 August 2017, Stockholm, Sweden
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-07-03 Created: 2017-07-03 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1907-7820

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