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  • 1.
    Simaki, Vasiliki
    et al.
    Lancaster University, UK ; Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    A two-step procedure to identify stance constructions in discourse from political blogs2019In: Corpora, ISSN 1749-5032, E-ISSN 1755-1676Article in journal (Refereed)
    Abstract [en]

    The Brexit Blog Corpus (BBC) is a collection of texts extracted from political blogs, which, in a recent study, was annotated according to a cognitive-functional stance framework by two independent annotators (Annotator A and B) using semantic criteria (Simaki et al. 2017). The goal was to label the stance or stances taken based on the overall meaning of a set of utterances. The annotators were not instructed to identify the lexical forms that were used to express the stances. In this study, we make use of those stance labelled utterances as a springboard to approach stance-taking in text from the opposite point of view, namely from how stance is realised through language. Our aim is to provide a description of the specific lexical elements used to express six stance categories, i.e., CONTRARIETY, HYPOTHETICALITY,  NECESSITY, PREDICTION, SOURCE OF KNOWLEDGE, and UNCERTAINTY. To this end, we followed a two-step experimental procedure. First, we performed a quantitative analysis of the stance labelled utterances in order to identify the lexical realisations of each stance category. Second, we carried out a meta-annotation of the data. Annotator B was instructed to single out the actual lexical forms of the constructions that triggered his semantic stance category decisions. This meta-annotation procedure made it possible for us to sift out the most salient lexical realisations of the constructions of each of the six category types on the basis of the qualitative assessments made by Annotator B. We then compared the results of the quantitative and the qualitative approaches, and we present a list of shared stance expressions for each stance category type.

  • 2.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Sentiment and Stance Visualization of Textual Data for Social Media2019Doctoral 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.

  • 3.
    Simaki, Vasiliki
    et al.
    Lancaster University, UK ; Lund University.
    Panagiotis, Simakis
    XPLAIN, Greece.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Detection of Stance-Related Characteristics in Social Media Text2018In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN '18), ACM Publications, 2018, article id 38Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative- and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data.

  • 4.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    DoSVis: Document Stance Visualization2018In: 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 (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. 

  • 5.
    Simaki, Vasiliki
    et al.
    Lancaster University,UK ; Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis2018In: ICAME Journal/International Computer Archive of Modern English, ISSN 0801-5775, E-ISSN 1502-5462, Vol. 42, no 1, p. 133-166Article in journal (Refereed)
    Abstract [en]

    This paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to highlight linguistic features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine linguistic similarities throughout the data set. We show that among the six stance categories in the corpus, contrariety and necessity are the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances. The latter has longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters.

  • 6.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    The State of the Art in Sentiment Visualization2018In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed)
    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. 

  • 7.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Potsdam University, Germany.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Stede, Manfred
    Potsdam University, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics2018In: 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 (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.

  • 8.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Potsdam University, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Stede, Manfred
    Potsdam University, Germany.
    Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models2018In: Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth / [ed] Adrien Ugon, Daniel Karlsson, Gunnar O. Klein, and Anne Moen, IOS Press, 2018, p. 366-370Conference paper (Refereed)
    Abstract [en]

    Arguments used when vaccination is debated on Internet discussion forums might give us valuable insights into reasons behind vaccine hesitancy. In this study, we applied automatic topic modelling on a collection of 943 discussion posts in which vaccine was debated, and six distinct discussion topics were detected by the algorithm. When manually coding the posts ranked as most typical for these six topics, a set of semantically coherent arguments were identified for each extracted topic. This indicates that topic modelling is a useful method for automatically identifying vaccine-related discussion topics and for identifying debate posts where these topics are discussed. This functionality could facilitate manual coding of salient arguments, and thereby form an important component in a system for computer-assisted coding of vaccine-related discussions. 

  • 9.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Visual Analysis of Sentiment and Stance in Social Media Texts2018In: EuroVis 2018 - Posters / [ed] Anna Puig and Renata Raidou, Eurographics - European Association for Computer Graphics, 2018, p. 49-51Conference paper (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.

  • 10.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Active Learning and Visual Analytics for Stance Classification with ALVA2017In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed)
    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.

  • 11.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Annotating speaker stance in discourse: the Brexit Blog Corpus2017In: Corpus linguistics and linguistic theory, ISSN 1613-7027, E-ISSN 1613-7035Article in journal (Refereed)
    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. 

  • 12.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. University of Potsdam, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Stede, Manfred
    University of Potsdam, Germany.
    Automatic detection of stance towards vaccination in online discussion forums2017In: Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017) / [ed] Jitendra Jonnagaddala, Hong-Jie Dai, and Yung-Chun Chang, Association for Computational Linguistics, 2017, p. 1-8Conference paper (Refereed)
    Abstract [en]

    A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance against or for vaccination, or as undecided. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance against vaccination from stance for vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Fu- ture work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features. 

  • 13.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Li, Yuan-Fang
    Monash University, Australia.
    Schreiber, Falk
    University of Konstanz, Germany ; Monash University, Australia.
    BioVis Explorer: A visual guide for biological data visualization techniques2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, article id e0187341Article in journal (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 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.

  • 14.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Simaki, Vasiliki
    Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Detection of Stance and Sentiment Modifiers in Political Blogs2017In: Speech and Computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings / [ed] Alexey Karpov, Rodmonga Potapova, and Iosif Mporas, Springer International Publishing , 2017, p. 302-311Conference paper (Refereed)
    Abstract [en]

    The automatic detection of seven types of modifiers was studied: Certainty, Uncertainty, Hypotheticality, Prediction, Recommendation, Concession/Contrast and Source. A classifier aimed at detecting local cue words that signal the categories was the most successful method for five of the categories. For Prediction and Hypotheticality, however, better results were obtained with a classifier trained on tokens and bi-grams present in the entire sentence. Unsupervised cluster features were shown useful for the categories Source and Uncertainty, when a subset of the training data available was used. However, when all of the 2,095 sentences that had been actively selected and manually annotated were used as training data, the cluster features had a very limited effect. Some of the classification errors made by the models would be possible to avoid by extending the training data set, while other features and feature representations, as well as the incorporation of pragmatic knowledge, would be required for other error types. 

  • 15.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University, Sweden.
    Simakis, Panagiotis
    XPLAIN, Greece.
    Paradis, Carita
    Lund University, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Identifying the Authors' National Variety of English in Social Media Texts2017In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP 2017 / [ed] Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Ivelina Nikolova, and Irina Temnikova, Stroudsburg, PA: Association for Computational Linguistics, 2017, p. 671-678Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a study for the identification of authors’ national variety of English in texts from social media. In data from Facebook and Twitter, information about the author’s social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and databased features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32%. The experimental results are evaluated, and the efficacy of the ranked features discussed.

  • 16.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Language Processing Components of the StaViCTA Project2017In: 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 (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. 

  • 17.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Li, Yuan-Fang
    Monash University, Australia.
    Schreiber, Falk
    University of Konstanz, Germany ; Monash University, Australia.
    MDS-based Visual Survey of Biological Data Visualization Techniques2017In: EuroVis 2017 - Posters / [ed] Anna Puig Puig and Tobias Isenberg, Eurographics - European Association for Computer Graphics, 2017, p. 85-87Conference paper (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. 

  • 18.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Stance Classification in Texts from Blogs on the 2016 British Referendum2017In: Speech and Computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings / [ed] Alexey Karpov, Rodmonga Potapova, and Iosif Mporas, Springer International Publishing , 2017, p. 700-709Conference paper (Refereed)
    Abstract [en]

    The problem of identifying and correctly attributing speaker stance in human communication is addressed in this paper. The data set consists of political blogs dealing with the 2016 British referendum. A cognitive-functional framework is adopted with data annotated for six notional stance categories: concession/contrariness, hypotheticality, need/ requirement, prediction, source of knowledge, and uncertainty. We show that these categories can be implemented in a text classification task and automatically detected. To this end, we propose a large set of lexical and syntactic linguistic features. These features were tested and classification experiments were implemented using different algorithms. We achieved accuracy of up to 30% for the six-class experiments, which is not fully satisfactory. As a second step, we calculated the pair-wise combinations of the stance categories. The concession/contrariness and need/requirement binary classification achieved the best results with up to 71% accuracy. 

  • 19.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Simaki, Vasiliki
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media2017Conference 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. 

  • 20.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    RISE SICS.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset2017In: Informatics, ISSN 2227-9709, Vol. 4, no 2, article id 11Article in journal (Refereed)
    Abstract [en]

    The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real world networks are multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related data sets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents, and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the IEEE VIS publications dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a Bag-of-Words index, and a word similarity matrix, all derived from the initial corpus and metadata. 

  • 21.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Swedish Institute of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Active Learning for Detection of Stance Components2016In: Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES '16) at COLING '16, Association for Computational Linguistics, 2016, p. 50-59Conference paper (Refereed)
    Abstract [en]

    Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition. 

  • 22.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Gavagai AB, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Marker Words for Negation and Speculation in Health Records and Consumer Reviews2016In: Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine (SMBM '16) / [ed] Mariana Neves, Fabio Rinaldi, Goran Nenadic, and Dietrich Rebholz-Schuhmann, CEUR-WS.org , 2016, Vol. 1650, p. 64-69Conference paper (Refereed)
    Abstract [en]

    Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation markers in the consumer review corpus, while detection of negation markers was unsuccessful in this genre. Also a setup in which models were trained on markers in consumer reviews, and applied on the clinical text genre, yielded low results. This shows that neither the trained models, nor the choice of appropriate machine learning algorithms and features, were transferable across the two text genres.

  • 23.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Methodology and Applications of Visual Stance Analysis: An Interactive Demo2016In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, p. 56-57Conference paper (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. 

  • 24.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    PAL, a tool for Pre-annotation and Active Learning2016In: Journal for Language Technology and Computational Linguistics, ISSN 0175-1336, E-ISSN 2190-6858, Vol. 31, no 1, p. 81-100Article in journal (Refereed)
    Abstract [en]

    Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming.

    The aim of “PAL, a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation.

    The output of the pre-annotation is provided in the annotation format of the annotation tool BRAT, but PAL is a stand-alone package that can be adapted to other formats. 

  • 25.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. Gavagai AB.
    Sahlgren, Magnus
    Gavagai AB.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Unshared Task: (Dis)agreement in Online Debates2016In: Proceedings of the 3rd Workshop on Argument Mining (ArgMining '16) at ACL '16, Association for Computational Linguistics, 2016, p. 154-159, article id W16-2818Conference paper (Refereed)
    Abstract [en]

    Topic-independent expressions for conveying agreement and disagreement were annotated in a corpus of web forum debates, in order to evaluate a classifier trained to detect these two categories. Among the 175 expressions annotated in the evaluation set, 163 were unique, which shows that there is large variation in expressions used. This variation might be one of the reasons why the task of automatically detecting the categories was difficult. F-scores of 0.44 and 0.37 were achieved by a classifier trained on 2,000 debate sentences for detecting sentence-level agreement and disagreement.

  • 26.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schamp-Bjerede, Teri
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena2016In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 15, no 2, p. 93-116Article in journal (Refereed)
    Abstract [en]

    Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

  • 27.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University .
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Text Annotations for Stance Classification with ALVA2016In: EuroVis Posters 2016 / [ed] Tobias Isenberg & Filip Sadlo, Eurographics - European Association for Computer Graphics, 2016, p. 49-51Conference paper (Refereed)
    Abstract [en]

    The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. 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. 

  • 28.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Cernea, Daniel
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Visualizing Excitement of Individuals and Groups2016In: Proceedings of the ACM IUI 2016 Workshop on Emotion and Visualization (EmoVis '16) / [ed] Andreas Kerren, Daniel Cernea, and Margit Pohl, Linköping, Sweden: Linköping University Electronic Press, 2016, p. 15-22Conference paper (Refereed)
    Abstract [en]

    Excitement or arousal is one of the main emotional dimensions that affects our lives on a daily basis. We win a tennis match, watch a great movie, get into an argument with a colleague—all of these are instances when most of us experience excitement, yet we do not pay much attention to it. Today, there are few systems that capture our excitement levels and even fewer that actually promote awareness of our most exciting moments. In this paper, we propose a visualization concept for representing individual and group-level excitement for emotional self-awareness and group-level awareness. The data used for the visualization is obtained from smart wristbands worn by each of the users. The visualization uses animated glyphs to generate a real-time representation for each individual’s excitement levels. We introduce two types of encodings for these glyphs: one focusing on capturing both the current excitement and the excitement history, as well as another focusing only on real-time values and previous peaks. The excitement levels are computed based on measurements of the user’s galvanic skin response and accelerometer data from the wristbands, allowing for a classification of the excitement levels into experienced (excitement without physical manifestation) and manifested excitement. A dynamic clustering of the individual glyphs supports the scalability of our visualization, while at the same time offering an overview of the group-level excitement and its distribution. The results of a preliminary evaluation suggest that the visualization allows users to intuitively and accurately perceive both individual and group-level excitement. 

  • 29.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. Gavagai AB.
    Schamp-Bjerede, Teri
    Lund University.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Detecting Speculations, Contrasts and Conditionals in Consumer Reviews2015In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '15): Short Paper Track / [ed] Alexandra Balahur, Erik van der Goot, Piek Vossen, and Andrés Montoyo, Association for Computational Linguistics , 2015, p. 162-168Conference paper (Refereed)
    Abstract [en]

    A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an F-score of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional. 

  • 30.
    Alfalahi, Alyaa
    et al.
    Stockholm University.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of Computer Science. Gavagai AB, Sweden.
    Ahlblom, Rickard
    Stockholm University.
    Baskalayci, Roza
    Stockholm University.
    Henriksson, Aron
    Stockholm University.
    Asker, Lars
    Stockholm University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Expanding a Dictionary of Marker Words for Uncertainty and Negation Using Distributional Semantics2015In: Proceedings of the 6th International Workshop on Health Text Mining and Information Analysis (Louhi '15): Short Paper Track / [ed] Cyril Grouin, Thierry Hamon, Aurélie Névéol, and Pierre Zweigenbaum, Association for Computational Linguistics , 2015, p. 90-96Conference paper (Refereed)
    Abstract [en]

    Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word. 

  • 31.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. Gavagai AB.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    Finding Infrequent Phenomena in Large Corpora Using Distributional Semantics2015In: Symposium on Methods and Linguistic Theories (MaLT '15), Bamberg, Germany, 27-28 November 2015, 2015Conference paper (Refereed)
  • 32.
    Schamp-Bjerede, Teri
    et al.
    Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    New Perspectives on Gathering, Vetting and Employing Big Data from Online Social Media: An Interdisciplinary Approach2015In: Abstracts Booklet, ICAME 36: Words, Words, Words – Corpora and Lexis, 2015, p. 153-155Conference paper (Refereed)
  • 33.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Text Visualization Techniques: Taxonomy, Visual Survey, and Community Insights2015In: Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15), / [ed] Shixia Liu, Gerik Scheuermann, and Shigeo Takahashi, IEEE, 2015, p. 117-121Conference paper (Refereed)
    Abstract [en]

    Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data. 

  • 34.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Rahimi, Afshin
    Hedges and Tweets: Certainty and Uncertainty in Epistemic Markers in Microblog Feeds2014In: Book of abstracts: 47th Annual Meeting of the Societas Linguistica Europaea 11–14 September 2014, Adam Mickiewicz University, Poznań, Poland, 2014, p. 199-199Conference paper (Refereed)
  • 35.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Text Visualization Browser: A Visual Survey of Text Visualization Techniques2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this poster, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. 

  • 36.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    The Signifier, Signified and Stance: Happy/Sad Emoticons as Emotionizers2014In: Book of Abstracts, IACS 2014, 2014, p. 219-219Conference paper (Refereed)
  • 37.
    Rahimi, Afshin
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    The StaViCTA Group Report for RepLab 2014: Reputation Dimensions Task2014In: Working Notes for CLEF 2014 Conference: Sheffield, UK, September 15-18, 2014 / [ed] Linda Cappellato, Nicola Ferro, Martin Halvey, Wessel Kraaij, CEUR-WS.org , 2014, p. 1519-1527Conference paper (Refereed)
    Abstract [en]

    In this paper we present our experiments on the RepLab 2014 Reputation Dimension task. RepLab is a competitive challenge for Reputation Management Systems. RepLab 2014’s reputation dimensions task focuses on categorization of Twitter messages with regard to standard reputation dimensions (such as performance, leadership, or innovation). Our approach only relies on the textual content of tweets and ignores both metadata and the content of URLs within tweets. We carried out several experiments focusing on different feature sets including bag of n-grams, distributional semantics features, and deep neural network representations. The results show that bag of bigram features with minimum frequency thresholding work quite well in reputation dimension task especially with regards to average F1 measure over all dimensions where two of our four submitted runs achieve highest and second highest scores. Our experiments also show that semi-supervised recursive autoencoders outperform other feature sets used in our experiments with regards to accuracy measure and is a promising subject of future research for improvements. 

  • 38.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Turning Face: Emoticons as Reinforcers/Attenuators2014Conference paper (Refereed)
  • 39.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Stance Markers in Online Social Media2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection. 

1 - 39 of 39
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