lnu.sePublications
Change search
Refine search result
1234 101 - 150 of 166
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 101.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Analysis of VINCI 2009–2017 Proceedings2018In: 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 (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.

  • 102.
    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. 

  • 103.
    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. 

  • 104.
    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.

  • 105.
    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.

  • 106.
    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.

  • 107.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). University of Potsdam, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts2018In: 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 (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.

  • 108. Lin, Xia
    et al.
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Zhang, Jiajie
    Challenges in Human-Centered Information Visualization: Introduction to the Special Issue2009In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 8, no 3, p. 137-138Article, review/survey (Other academic)
  • 109. Mario, Albrecht
    et al.
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Klein, Karsten
    Kohlbacher, Oliver
    Mutzel, Petra
    Paul, Wolfgang
    Schreiber, Falk
    Wybrow, Michael
    A Graph-drawing Perspective to Some Open Problems in Molecular Biology2008Report (Other academic)
    Abstract [en]

    Much of the biological data generated and analyzed in the life sciences can be interpreted and represented by graphs. Many general and special-purpose tools and libraries are available for laying out and drawing graphs, but they are either not adequate for handling large graphs or do not adhere to the special drawing conventions and recognized layouts of biological networks. In this paper, we describe some representative use cases that demonstrate the need for advanced algorithms for presenting, exploring, evaluating, and comparing biological network data.

  • 110.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Efficient Dynamic Time Warping for Big Data Streams2019In: Proceedings of the IEEE International Conference on Big Data (Big Data '18): Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management / [ed] Abe, N; Liu, H; Pu, C; Hu, X; Ahmed, N; Qiao, M; Song, Y; Kossmann, D; Liu, B; Lee, K; Tang, J; He, J; Saltz, J, IEEE, 2019, p. 2924-2929Conference paper (Refereed)
    Abstract [en]

    Many common data analysis and machine learning algorithms for time series, such as classification, clustering, or dimensionality reduction, require a distance measurement between pairs of time series in order to determine their similarity. A variety of measures can be found in the literature, each with their own strengths and weaknesses, but the Dynamic Time Warping (DTW) distance measure has occupied an important place since its early applications for the analysis and recognition of spoken word. The main disadvantage of the DTW algorithm is, however, its quadratic time and space complexity, which limits its practical use to relatively small time series. This issue is even more problematic when dealing with streaming time series that are continuously updated, since the analysis must be re-executed regularly and with strict running time constraints. In this paper, we describe enhancements to the DTW algorithm that allow it to be used efficiently in a streaming scenario by supporting an append operation for new time steps with a linear complexity when an exact, error-free DTW is needed, and even better performance when either a Sakoe-Chiba band is used, or when a sliding window is the desired range for the data. Our experiments with one synthetic and four natural data sets have shown that it outperforms other DTW implementations and the potential errors are, in general, much lower than another state-of-the-art approximated DTW technique.

  • 111.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kruiger, Johannes F.
    University of Groningen, The Netherlands ; École Nationale de l’Aviation Civile, France.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Telea, Alexandru C.
    University of Groningen, The Netherlands.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks2017In: EuroVis 2017 - Short Papers / [ed] Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll, Eurographics - European Association for Computer Graphics, 2017, p. 13-17Conference paper (Refereed)
    Abstract [en]

    The analysis of Multivariate Networks (MVNs) can be approached from two different perspectives: a multidimensional one, consisting of the nodes and their multiple attributes, or a relational one, consisting of the network’s topology of edges. In order to be comprehensive, a visual representation of an MVN must be able to accomodate both. In this paper, we propose a novel approach for the visualization of MVNs that works by combining these two perspectives into a single unified model, which is used as input to a dimensionality reduction method. The resulting 2D embedding takes into consideration both attribute- and edge-based similarities, with a user-controlled trade-off. We demonstrate our approach by exploring two real-world data sets: a co-authorship network and an open-source software development project. The results point out that our method is able to bring forward features of MVNs that could not be easily perceived from the investigation of the individual perspectives only. 

  • 112.
    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. 

  • 113. Meier, Holger A.
    et al.
    Schlemmer, Michael
    Wagner, Christian
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Hagen, Hans
    Kuhl, Ellen
    Steinmann, Paul
    Visualization of Particle Interactions in Granular Media2008In: IEEE Transactions on Visualization & Computer Graphics, ISSN 1077-2626, Vol. 14, no 5, p. 1110-1125Article in journal (Refereed)
    Abstract [en]

    Interaction between particles in so-called granular media, such as soil and sand, plays an important role in the context of geomechanical phenomena and numerous industrial applications. A two scale homogenization approach based on a micro and a macro scale level is briefly introduced in this paper. Computation of granular material in such a way gives a deeper insight into the context of discontinuous materials and at the same time reduces the computational costs. However, the description and the understanding of the phenomena in granular materials are not yet satisfactory. A sophisticated problem-specific visualization technique would significantly help to illustrate failure phenomena on the microscopic level. As main contribution, we present a novel 2D approach for the visualization of simulation data, based on the above outlined homogenization technique. Our visualization tool supports visualization on micro scale level as well as on macro scale level. The tool shows both aspects closely arranged in form of multiple coordinated views to give users the possibility to analyze the particle behavior effectively. A novel type of interactive rose diagrams was developed to represent the dynamic contact networks on the micro scale level in a condensed and efficient way.

  • 114.
    Mporas, Iosif
    et al.
    Univ Hertfordshire, UK.
    Simaki, Vasiliki
    Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Paraskevas, Michael
    Univ Peloponnese, Greece.
    Special Issue on Natural Language Processing for Social Media Analysis2020In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 29, no 2, p. 1-2, article id 2002001Article in journal (Other academic)
  • 115. Müldner, Tomasz
    et al.
    Shakshuki, Elhadi
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Algorithm Education Using Structured Hypermedia2008In: Strategic Application of Distance Learning Technologies, Information Science Reference , 2008Chapter in book (Other (popular science, discussion, etc.))
  • 116. Müldner, Tomasz
    et al.
    Shakshuki, Elhadi
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Shen, Zhinan
    Bai, Xiaoguang
    Using Structured Hypermedia to Explain Algorithms2005In: Proceedings of the 3rd IADIS International Conference e-Society '05, IADIS , 2005, p. 499-503Conference paper (Refereed)
    Abstract [en]

    Most systems designed to teach algorithms using visualization and animation techniques have not proved to be educationally effective. To satisfy this aim, some recently built systems use a hypermedia environment to provide knowledge and context to explain algorithms. In this paper, we describe a system called Structured Hypermedia Algorithm Explanation (SHALEX), which provides several novel and important features. In particular, our hypermedia environment can reflect the structure of an algorithm. We define this structure as a directed graph of abstractions, where each abstraction is designed to focus on a single operation used directly or indirectly in the algorithm. This way an algorithm may be studied top-down, bottom-up, or using a mix of the two. In addition, SHALEX includes a student model to provide spatial and temporal links, and to support evaluations and adaptations.

  • 117. Olech, Peter-Scott
    et al.
    Cernea, Daniel
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Thelen, Sebastian
    Ebert, Achim
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Hagen, Hans
    V.I.P.: Supporting Digital Earth Ideas through Visualization, Interaction and Presentation Screens2010In: Proceedings of the 7th Taipei International Digital Earth Symposium (TIDES '10). , 2010Conference paper (Refereed)
  • 118.
    Pohl, Margit
    et al.
    Vienna University of Technology, Austria.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Human Factors and Multilayer Networks2019In: Workshop on Visualization of Multilayer Networks (MNLVIS '19) at IEEE VIS '19, October 21, 2019, Vancouver, BC, Canada, 2019Conference paper (Refereed)
    Abstract [en]

    Analysts of specific application domains, such as experts in systems biology or social scientists, are often interested to visually analyze a number of different network structures in conjunction, for example by using various visual structures of so-called multilayer networks. From the perspective of the human analyst, a sufficient perception and, consequently, a good understanding of those visual representations of multilayer networks is a non-trivial and often challenging task. Despite this practical importance and the clearly interesting visualization challenges, only few evaluation studies exist that investigate usability and cognitive issues of complex networks or, more specifically, multilayer networks. In this position paper, we address two main goals. On the one hand, we discuss existing studies from the fields of human-computer interaction and cognitive psychology that could inform the designers of multilayer network visualization in the future. On the other hand, we formulate first tentative recommendations for the design of multilayer networks, identify open issues in this context, and clarify possible future directions of research.

    Download full text (pdf)
    fulltext
  • 119.
    Rahimi, Afshin
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University, Sweden.
    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. 

  • 120. Rohrschneider, Markus
    et al.
    Heine, Christian
    Reichenbach, André
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering.
    Scheuermann, Gerik
    A Novel Grid-based Visualization Approach for Metabolic Networks with Advanced Focus&Context View2009In: Graph Drawing: 17th International Symposium, GD 2009, Chicago, IL, USA, September 22-25, 2009. Revised Papers / [ed] David Eppstein and Emden R. Gansner, Berlin Heidelberg New Work: Springer, 2009, p. 268-279Chapter in book (Refereed)
    Abstract [en]

    The universe of biochemical reactions in metabolic pathwayscan be modeled as a complex network structure augmented with domain specific annotations. Based on the functional properties of the involved reactions, metabolic networks are often clustered into so-called pathways inferred from expert knowledge. To support the domain expert in the exploration and analysis process, we follow the well-known Table Lens metaphor with the possibility to select multiple foci.

    In this paper, we introduce a novel approach to generate an interactive layout of such a metabolic network taking its hierarchical structure into account and present methods for navigation and exploration that preserve the mental map. The layout places the network nodes on a fixed rectilinear grid and routes the edges orthogonally between the node positions. Our approach supports bundled edge routes heuristically minimizing a given cost function based on the number of bends, the number of edge crossings and the density of edges within a bundle.

  • 121.
    Rohrschneider, Markus
    et al.
    Leipzig University.
    Ullrich, Alexander
    Leipzig University.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Stadler, Peter F.
    Leipzig University, Germany.
    Scheuermann, Gerik
    Leipzig University, Germany.
    Visual Network Analysis of Dynamic Metabolic Pathways2010In: Advances in Visual Computing: 6th International Symposium, ISVC 2010, Las Vegas, NV, USA, November 29-December 1, 2010. Proceedings, Part I / [ed] George Bebis, Richard Boyle, Bahram Parvin et al., Berlin Heidelberg New Work: Springer, 2010, p. 316-327Conference paper (Refereed)
    Abstract [en]

    We extend our previous work on the exploration of static metabolic networks to evolving, and therefore dynamic, pathways. We apply our visualization software to data from a simulation of early metabolism. Thereby, we show that our technique allows us to test and argue for or against different scenarios for the evolution of metabolic pathways. This supports a profound and efficient analysis of the structure and properties of the generated metabolic networks and its underlying components, while giving the user a vivid impression of the dynamics of the system. The analysis process is inspired by Ben Shneiderman’s mantra of information visualization. For the overview, user-defined diagrams give insight into topological changes of the graph as well as changes in the attribute set associated with the participating enzymes, substances and reactions. This way, “interesting features” in time as well as in space can be recognized. A linked view implementation enables the navigation into more detailed layers of perspective for in-depth analysis of individual network configurations.

  • 122. Rößling, Guido
    et al.
    Malmi, Lauri
    Clancy, Michael
    Joy, Mike
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Korhonen, Ari
    Moreno, Andrés
    Naps, Tom
    Öchsle, Rainer
    Radenski, Atanas
    Ross, Rockford J.
    Velázquez-Iturbide, J. Ángel
    Enhancing Learning Management Systems to Better Support Computer Science Education2008In: ACM SIGCSE Bulletin: inroads, ISSN 0097-8418, Vol. 40, no 4, p. 142-166Article in journal (Refereed)
    Abstract [en]

    Many individual instructors -- and, in some cases, entire universities -- are gravitating towards the use of comprehensive learning management systems (LMSs), such as Blackboard and Moodle, for managing courses and enhancing student learning. As useful as LMSs are, they are short on features that meet certain needs specific to computer science education. On the other hand, computer science educators have developed--and continue to develop-computer-based software tools that aid in management, teaching, and/or learning in computer science courses. In this report we provide an overview of current CS specific on-line learning resources and guidance on how one might best go about extending an LMS to include such tools and resources. We refer to an LMS that is extended specifically for computer science education as a Computing Augmented Learning Management System, or CALMS. We also discuss sound pedagogical practices and some practical and technical principles for building a CALMS. However, we do not go into details of creating a plug-in for some specific LMS. Further, the report does not favor one LMS over another as the foundation for a CALMS.

  • 123. Rößling, Guido
    et al.
    Naps, Tom
    Hall, Marc S.
    Karavirta, Villa
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Leska, Charles
    Moreno, Andrés
    Öchsle, Rainer
    Rodger, Susan H.
    Urquiza-Fuentes, Jaime
    Velázquez-Iturbide, J. Ángel
    Merging Interactive Visualizations with Hypertextbooks and Course Management2006In: ACM SIGCSE Bulletin: inroads, ISSN 0097-8418, Vol. 38, no 4, p. 166-181Article in journal (Refereed)
    Abstract [en]

    As a report of a working group at ITiCSE 2006, this paper provides a vision of how visualizations and the software that generates them may be integrated into hypertextbooks and course management systems. This integration generates a unique synergy that we call a Visualization-based Computer Science Hypertextbook (VizCoSH). By borrowing features of both traditional hypertextbooks and course management systems, VizCoSHs become delivery platforms that address some of the reasons why visualizations have failed to find widespread use in education.

    The heart of the paper describes these features and explains, from both a student and teacher perspective, how each feature adds educational value to a visualization. In some cases, this value focuses on pedagogical issues, taking advantage of known strategies for making visualizations more engaging and effective. In other cases, the emphasis is on making it easier for teachers to use visualizations. A set of possible use scenarios and approaches for increasing interest in adopting a VizCoSH are also presented.

  • 124.
    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)
  • 125.
    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)
  • 126.
    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)
  • 127.
    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)
  • 128. Schreiber, Falk
    et al.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Börner, Katy
    Hagen, Hans
    Zeckzer, Dirk
    Heterogeneous Networks on Multiple Levels2014In: MULTIVARIATE NETWORK VISUALIZATION / [ed] Kerren, A; Purchase, HC; Ward, MO, Springer, 2014, p. 175-206Conference paper (Refereed)
    Abstract [en]

    Heterogeneous networks and multi-level networks occur in several application fields where their integration, combination, comparison, analysis, and visualization poses major challenges. In this chapter, we analyze the general characteristics of this type of data and identify examples in three application domains: biology, social sciences, and software engineering. Conceptually, we focus on sets of multivariate networks at two or more levels. Each level may describe a specific scale, and within each level several related heterogeneous networks are represented. We allow n:m mappings within the same level, but only 1:n mappings across levels that must be consecutive. This leads to a structured data set that is the basis for further visual analysis. Our chapter ends with ideas to visualize those networks together with the relationships between them and highlights research challenges.

  • 129. Shakshuki, Elhadi
    et al.
    Kerren, Andreas
    Växjö University, Faculty of Mathematics/Science/Technology, School of Mathematics and Systems Engineering. Computer Science.
    Müldner, Tomasz
    Web-based Structured Hypermedia Algorithm Explanation System2007In: International Journal of Web Information Systems, ISSN 1744-0084, Vol. 3, no 3, p. 179-197Article in journal (Refereed)
    Abstract [en]

    Purpose – Development of a system called Structured Hypermedia Algorithm Explanation (SHALEX), as a remedy for the limitations existing within the current traditional algorithm animation systems. SHALEX provides several novel features, such as use of invariants, reflection of the high-level structure of an algorithm rather than low-level steps, and support for programming the algorithm in any procedural or object-oriented programming language.

    Design/methodology/approach – By defining the structure of an algorithm as a directed graph of abstractions, algorithms may be studied top-down, bottom-up, or using a mix of the two. In addition, SHALEX includes a learner model to provide spatial links, and to support evaluations and adaptations.

    Findings – Evaluations of traditional algorithm animation systems designed to teach algorithms in higher education or in professional training show that such systems have not achieved many expectations of their developers. One reason for this failure is the lack of stimulating learning environments which support the learning process by providing features such as multiple levels of abstraction, support for hypermedia, and learner-adapted visualizations. SHALEX supports these environments, and in addition provides persistent storage that can be used to analyze students’ performance. In particular, this storage can be used to represent a student model that supports adaptive system behavior.

    Research limitations/implications – SHALEX is being implemented and tested by the authors and a group of students. The tests performed so far have shown that SHALEX is a very useful tool. In the future we plan additional quantitative evaluation to compare SHALEX with other AA systems and/or the concept keyboard approach.

    Practical implications – SHALEX has been implemented as a web-based application using the client-server architecture. Therefore, students can use SHALEX to learn algorithms through both distance education and in the classroom setting.

    Originality/value – This paper presents a novel algorithm explanation system for users who wish to learn algorithms.

    Keywords: Interactive learning environments, multimedia/hypermedia systems, programming and programming languages, Navigation

    Article Type: Research paper

  • 130.
    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.

  • 131.
    Simaki, Vasiliki
    et al.
    Lancaster University, UK;Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    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 1755-1676, Vol. 14, no 3, p. 379-405Article 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.

  • 132.
    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.

  • 133.
    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.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), 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, 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. 

  • 134.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Gavagai, Sweden.
    Sahlgren, Magnus
    Gavagai, Sweden.
    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. 

  • 135.
    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.

  • 136.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). The Institute for Language and Folklore, Sweden;Hokkaido University, Japan.
    Ahltorp, Magnus
    The Institute for Language and Folklore, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Rzepka, Rafal
    Hokkaido University, Japan;RIKEN Center for Advanced Intelligence Project (AIP), Japan.
    Araki, Kenji
    Hokkaido University, Japan.
    Application of a topic model visualisation tool to a second language2019In: CLARIN 2019 Book of absracts, CLARIN, Common Language Resources and Technology Infrastructure , 2019Conference paper (Refereed)
    Abstract [en]

    We explored adaptions required for applying a topic modelling tool to a language that is very different from the one for which the tool was originally developed. The tool, which enables text analysis on the output of topic modelling, was developed for English, and we here applied it on Japanese texts. As white space is not used for indicating word boundaries in Japanese, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation, before the texts could be imported into the tool. The tool was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese.

    Download full text (pdf)
    fulltext
  • 137.
    Skeppstedt, Maria
    et al.
    The Institute for Language and Folklore, Sweden.
    Ahltorp, Magnus
    The Institute for Language and Folklore, Sweden.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Rzepka, Rafal
    Hokkaido University, Japan;RIKEN Center for Advanced Intelligence Project (AIP), Japan.
    Araki, Kenji
    Hokkaido University, Japan.
    Topic modelling applied to a second language: A language adaption and tool evaluation study2020In: Selected Papers – CLARIN Annual Conference 2019, 2020Conference paper (Refereed)
    Abstract [en]

    The Topics2Themes tool, which enables text analysis on the output of topic modelling, was originally developed for the English language. In this study, we explored and evaluated adaptations required for applying the tool to Japanese texts. That is, we adapted Topics2Themes to a language that is very different from the one for which the tool was originally developed. To apply Topics2Themes to Japanese texts, in which white space is not used for indicating word boundaries, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation. Topics2Themes was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese. To evaluate the adaptation to a second language, as well as the reading support, we applied the tool to a corpus consisting of short Japanese texts. Twelve different topics were automatically identified, and a total of 183 texts representative for the twelve topics were extracted. A learner of Japanese carried out a manual analysis of these representative texts, and identified 35 reoccurring, fine-grained themes.

  • 138.
    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. 

  • 139.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). University of Potsdam, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Stede, Manfred
    University of Potsdam, Germany.
    Finding Reasons for Vaccination Hesitancy: Evaluating Semi-Automatic Coding of Internet Discussion Forums2019In: MEDINFO 2019: Health and Wellbeing e-Networks for All: Proceedings of the 17th World Congress on Medical and Health Informatics / [ed] Lucila Ohno-Machado and Brigitte Séroussi, IOS Press, 2019, p. 348-352Conference paper (Refereed)
    Abstract [en]

    Computer-assisted text coding can facilitate the analysis of large text collections. To evaluate the functionality of providing an analyst with a ranked list of suggestions for suitable text codes, we used a data set of discussion posts, which had been manually coded for reasons given for taking a stance on the topic of vaccination. We trained a logistic regression classifier to rank these reasons according to the probability that they would be present in the post. The approach was evaluated for its ability to include the expected reasons among the n top-ranked reasons, using an n between 1 and 6. The logistic regression-based ranking was more effective than the baseline, which ranked reasons according to their frequency in the training data. To provide such a list of possible codes, ranked by logistic regression, could therefore be a useful feature in a tool for text coding.

  • 140.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. University of Potsdam, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Stede, Manfred
    University of Potsdam, 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. 

  • 141.
    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. 

    Download full text (pdf)
    fulltext
  • 142.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. University of Potsdam, Germany.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Stede, Manfred
    University of Potsdam, 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.

  • 143.
    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.

  • 144.
    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. 

  • 145.
    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)
    Download full text (pdf)
    fulltext
  • 146.
    Skeppstedt, Maria
    et al.
    The Institute for Language and Folklore, Sweden.
    Rzepka, Rafal
    Hokkaido University, Japan.
    Araki, Kenji
    Hokkaido University, Japan.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Visualising and Evaluating the Effects of Combining Active Learning with Word Embedding Features2019In: Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), German Society for Computational Linguistics and Language Technology (GSCL) , 2019, p. 91-100Conference paper (Refereed)
    Abstract [en]

    A tool that enables the use of active learning, as well as the incorporation of word embeddings, was evaluated for its ability to decrease the training data set size required for a named entity recognition model. Uncertainty-based active learning and the use of word embeddings led to very large performance improvements on small data sets for the entity categories PERSON and LOCATION. In contrast, the embedding features used were shown to be unsuitable for detecting entities belonging to the ORGANISATION category. The tool was also extended with functionality for visualising the usefulness of the active learning process and of the word embeddings used. The visualisations provided were able to indicate the performance differences between the entities, as well as differences with regards to usefulness of the embedding features.

    Download full text (pdf)
    fulltext
  • 147.
    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. 

  • 148.
    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.

  • 149.
    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. 

  • 150.
    Skeppstedt, Maria
    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.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), 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, 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. 

1234 101 - 150 of 166
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf