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Active Learning and Visual Analytics for Stance Classification with ALVA
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-1907-7820
Lund University.ORCID iD: 0000-0002-7240-9003
Swedish Research Institute (RISE SICS).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2017 (English)In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
New York, NY, USA: ACM Publications, 2017. Vol. 7, no 3, article id 14
Keywords [en]
visualization, stance visualization, active learning, text visualization, sentiment visualization, annotation, visual analytics, sentiment analysis, stance analysis, NLP, text analytics
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-67173DOI: 10.1145/3132169ISI: 000414322200005OAI: oai:DiVA.org:lnu-67173DiVA, id: diva2:1129673
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2017-08-05 Created: 2017-08-05 Last updated: 2018-04-04Bibliographically approved

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Kucher, KostiantynKerren, Andreas

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