Kostiantyn Kucher
Maria Skeppstedt
Andreas Kerren
ABSTRACT
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.
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Digital Library
- Wenwen Dou and Shixia Liu. 2016. Topic- and Time-Oriented Visual Text Analysis. IEEE Computer Graphics and Applications 36, 4 (July 2016), 8--13.Google ScholarCross Ref
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- Kostiantyn Kucher, Carita Paradis, and Andreas Kerren. 2018. The State of the Art in Sentiment Visualization. Computer Graphics Forum 37, 1 (Feb. 2018), 71--96.Google ScholarCross Ref
- Maria Skeppstedt, Andreas Kerren, and Manfred Stede. 2018. Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models. Studies in Health Technology and Informatics 247 (2018), 366--370. Proceedings of the 29th Medical Informatics Europe Conference (MIE '18).Google Scholar
- Maria Skeppstedt, Kostiantyn Kucher, Manfred Stede, and Andreas Kerren. 2018. Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics. In Proceedings of the 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources at LREC '18 (VisLR III). European Language Resources Association (ELRA), Paris, France. http://lrec-conf.org/workshops/lrec2018/W16/summaries/2_W16.htmlGoogle Scholar
- Maria Skeppstedt, Vasiliki Simaki, Carita Paradis, and Andreas Kerren. 2017. Detection of Stance and Sentiment Modifiers in Political Blogs. In Proceedings of the International Conference on Speech and Computer (SPECOM '17). Springer, 302--311.Google ScholarCross Ref
- Parinaz Sobhani, Diana Inkpen, and Stan Matwin. 2015. From Argumentation Mining to Stance Classification. In Proceedings of the 2nd Workshop on Argumentation Mining. Association for Computational Linguistics, Denver, CO, 67--77. http://aclweb.org/anthology/W15-0509Google ScholarCross Ref
- John Stasko, Carsten Görg, and Zhicheng Liu. 2008. Jigsaw: Supporting Investigative Analysis through Interactive Visualization. Information Visualization 7, 2 (2008), 118--132. Google ScholarCross Ref
Index Terms
- Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
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