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Kucher, K. (2019). Sentiment and Stance Visualization of Textual Data for Social Media. (Doctoral dissertation). Växjö, Sweden: Linnaeus University Press
Open this publication in new window or tab >>Sentiment and Stance Visualization of Textual Data for Social Media
2019 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Växjö, Sweden: Linnaeus University Press, 2019. p. 264
Series
Linnaeus University Dissertations ; 347/2019
Keywords
stance visualization, sentiment visualization, text visualization, stance analysis, sentiment analysis, opinion mining, visualization, interaction, visual analytics, NLP, text mining, text analytics, social media
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-81081 (URN)978-91-88898-47-0 (ISBN)978-91-88898-48-7 (ISBN)
Public defence
2019-04-15, Wicksell, Hus K, Växjö, 09:15 (English)
Opponent
Supervisors
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2019-03-19 Created: 2019-03-14 Last updated: 2019-07-30Bibliographically approved
Kucher, K. & Kerren, A. (2019). Text Visualization Revisited: The State of the Field in 2019. In: João M. Pereira and Renata G. Raidou (Ed.), Posters of the 21th EG/VGTC Conference on Visualization (EuroVis '19): . Paper presented at The 21th EG/VGTC Conference on Visualization (EuroVis '19), Porto, Portugal, 3-7 June, 2019 (pp. 29-31). Eurographics - European Association for Computer Graphics
Open this publication in new window or tab >>Text Visualization Revisited: The State of the Field in 2019
2019 (English)In: Posters of the 21th EG/VGTC Conference on Visualization (EuroVis '19) / [ed] João M. Pereira and Renata G. Raidou, Eurographics - European Association for Computer Graphics, 2019, p. 29-31Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2019
Keywords
Visual analytics, visualization, information visualization, text visualization
National Category
Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-82464 (URN)10.2312/eurp.20191138 (DOI)978-3-03868-088-8 (ISBN)
Conference
The 21th EG/VGTC Conference on Visualization (EuroVis '19), Porto, Portugal, 3-7 June, 2019
Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-09-24
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)2-s2.0-85055468154 (Scopus ID)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: 2019-08-29Bibliographically 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)2-s2.0-85055567433 (Scopus ID)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: 2019-08-29Bibliographically 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)2-s2.0-85047909778 (Scopus ID)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: 2019-05-27Bibliographically 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 ()2-s2.0-85051505723 (Scopus ID)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2019-08-29Bibliographically 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
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, 2018, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

While the task of manually extracting arguments from large collections of opinionated text is an intractable one, a tool for computerassisted extraction can (i) 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 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, 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, 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: 2018-10-15Bibliographically approved
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 ()2-s2.0-85032958347 (Scopus ID)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-08-05 Created: 2017-08-05 Last updated: 2019-08-29Bibliographically 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: 2019-08-28
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1907-7820

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