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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
Keyword
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
Keyword
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: : . 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.
Open this publication in new window or tab >>Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics
2018 (English)Conference 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. 

Keyword
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)
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
Note

TO BE PUBLISHED!

Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-02-15
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
Keyword
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
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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. 

Keyword
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.

Keyword
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
Keyword
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
Kerren, A., Kucher, K., Li, Y.-F. & Schreiber, F. (2017). MDS-based Visual Survey of Biological Data Visualization Techniques. In: Anna Puig Puig and Tobias Isenberg (Ed.), EuroVis 2017 - Posters: . Paper presented at The 19th EG/VGTC Conference on Visualization (EuroVis '17), Barcelona, Spain, 12-16 June, 2017 (pp. 85-87). Eurographics - European Association for Computer Graphics
Open this publication in new window or tab >>MDS-based Visual Survey of Biological Data Visualization Techniques
2017 (English)In: EuroVis 2017 - Posters / [ed] Anna Puig Puig and Tobias Isenberg, Eurographics - European Association for Computer Graphics, 2017, p. 85-87Conference paper, Poster (with or without abstract) (Refereed)
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 biological data visualization have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers as well as visualization researchers. To address this challenge, we have reviewed visualization research for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. 

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2017
Keyword
Visualization, Survey, Multidimensional Scaling, Dimensionality Reduction, Biological Data, BioVis
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-62608 (URN)10.2312/eurp.20171175 (DOI)978-3-03868-044-4 (ISBN)
Conference
The 19th EG/VGTC Conference on Visualization (EuroVis '17), Barcelona, Spain, 12-16 June, 2017
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-04-26 Created: 2017-04-26 Last updated: 2018-01-13Bibliographically approved
Martins, R. M., Simaki, V., Kucher, K., Paradis, C. & Kerren, A. (2017). StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media. In: : . Paper presented at 2nd Workshop on Visualization for the Digital Humanities (VIS4DH '17) at IEEE VIS '17, October 2017, Phoenix, Arizona, USA.
Open this publication in new window or tab >>StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. 

Keyword
Stance Visualization, Sentiment Analysis, Digital Humanities, Visual Analytics, Social Media Text
National Category
Human Computer Interaction Computer Sciences Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-67320 (URN)
Conference
2nd Workshop on Visualization for the Digital Humanities (VIS4DH '17) at IEEE VIS '17, October 2017, Phoenix, Arizona, USA
Projects
StaViCTADISA-DH
Funder
Swedish Research Council, 2012-5659
Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2018-02-01
Kucher, K., Kerren, A., Paradis, C. & Sahlgren, M. (2016). Methodology and Applications of Visual Stance Analysis: An Interactive Demo. In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts. Paper presented at International Symposium on Digital Humanities, Växjö, Sweden, November 7-8, 2016 (pp. 56-57). Linnaeus University
Open this publication in new window or tab >>Methodology and Applications of Visual Stance Analysis: An Interactive Demo
2016 (English)In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, p. 56-57Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.

Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. 

Place, publisher, year, edition, pages
Linnaeus University, 2016
Keyword
Digital humanities, Stance, Visualization, Interaction, NLP, Visual analytics, Annotation, Classifier training
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-57763 (URN)
Conference
International Symposium on Digital Humanities, Växjö, Sweden, November 7-8, 2016
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2016-11-01 Created: 2016-11-01 Last updated: 2018-01-13
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1907-7820

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