DoSVis : Document stance visualization
(2018) 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018 3. p.168-175- Abstract
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... (More)
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.
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- author
- Kucher, Kostiantyn ; Paradis, Carita LU and Kerren, Andreas
- organization
- publishing date
- 2018-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Information Visualization, Interaction, Sentiment Analysis, Sentiment Visualization, Stance Analysis, Stance Visualization, Text Analytics, Text Visualization
- host publication
- Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
- volume
- 3
- pages
- 8 pages
- publisher
- SciTePress
- conference name
- 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018
- conference location
- Funchal, Madeira, Portugal
- conference dates
- 2018-01-27 - 2018-01-29
- external identifiers
-
- scopus:85047909778
- ISBN
- 9789897582899
- language
- English
- LU publication?
- yes
- id
- 4ef5800c-6c85-4d60-bce6-428458ade958
- date added to LUP
- 2018-06-15 13:57:01
- date last changed
- 2022-03-25 02:39:18
@inproceedings{4ef5800c-6c85-4d60-bce6-428458ade958, abstract = {{<p>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.</p>}}, author = {{Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas}}, booktitle = {{Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}}, isbn = {{9789897582899}}, keywords = {{Information Visualization; Interaction; Sentiment Analysis; Sentiment Visualization; Stance Analysis; Stance Visualization; Text Analytics; Text Visualization}}, language = {{eng}}, month = {{01}}, pages = {{168--175}}, publisher = {{SciTePress}}, title = {{DoSVis : Document stance visualization}}, volume = {{3}}, year = {{2018}}, }