Visual Analysis of Sentiment and Stance in Social Media Texts
Kucher, Kostiantyn ; Paradis, Carita LU
and Kerren, Andreas
(2018)
EuroVis 2018
- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/182529d8-afa1-4ec2-8031-70550f896ad2
- author
- Kucher, Kostiantyn
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2018-06-08
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- visualization, visual analytics, text mining, sentiment, stance, Natural language processing
- conference name
- EuroVis 2018
- conference location
- Brno, Czech Republic
- conference dates
- 2018-06-04 - 2018-06-08
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- 182529d8-afa1-4ec2-8031-70550f896ad2
- date added to LUP
- 2018-04-24 19:56:50
- date last changed
- 2019-03-08 02:28:58
@misc{182529d8-afa1-4ec2-8031-70550f896ad2,
abstract = {{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.}},
author = {{Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas}},
keywords = {{visualization; visual analytics; text mining; sentiment; stance; Natural language processing}},
language = {{eng}},
month = {{06}},
title = {{Visual Analysis of Sentiment and Stance in Social Media Texts}},
year = {{2018}},
}