Visual analysis of stance markers in online social media
(2015) 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 p.259-260- Abstract
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... (More)
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.
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- author
- Kucher, Kostiantyn ; Kerren, Andreas ; Paradis, Carita LU and Sahlgren, Magnus
- organization
- publishing date
- 2015-02-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- interaction, NLP, sentiment analysis, stance analysis, text analytics, text visualization, time-series, Visualization
- host publication
- 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 : Proceedings. Paris, France, 9-14 October 2014 - Proceedings. Paris, France, 9-14 October 2014
- article number
- 7042519
- pages
- 2 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
- conference location
- Paris, France
- conference dates
- 2014-11-09 - 2014-11-14
- external identifiers
-
- scopus:84929460615
- ISBN
- 9781479962273
- DOI
- 10.1109/VAST.2014.7042519
- language
- English
- LU publication?
- yes
- id
- 9b003442-7d54-4cdd-8351-2746a27726d9
- date added to LUP
- 2016-05-17 10:48:37
- date last changed
- 2022-02-06 20:50:01
@inproceedings{9b003442-7d54-4cdd-8351-2746a27726d9, abstract = {{<p>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.</p>}}, author = {{Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus}}, booktitle = {{2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 : Proceedings. Paris, France, 9-14 October 2014}}, isbn = {{9781479962273}}, keywords = {{interaction; NLP; sentiment analysis; stance analysis; text analytics; text visualization; time-series; Visualization}}, language = {{eng}}, month = {{02}}, pages = {{259--260}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Visual analysis of stance markers in online social media}}, url = {{http://dx.doi.org/10.1109/VAST.2014.7042519}}, doi = {{10.1109/VAST.2014.7042519}}, year = {{2015}}, }