Visual Analysis of Text Annotations for Stance Classification with ALVA
Kucher, Kostiantyn ; Kerren, Andreas ; Paradis, Carita LU
and Sahlgren, Magnus
(2016)
EuroVis 2016, The 18th EG/VGTC Conference on Visualization
p.49-51
- Abstract
- The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided... (More)
- The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c13b1378-ff2d-457d-86c3-a19f55180968
- author
- Kucher, Kostiantyn
; Kerren, Andreas
; Paradis, Carita
LU
and Sahlgren, Magnus
- organization
- publishing date
- 2016-04-28
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- EuroVis Posters 2016
- editor
- Isenberg, Tobias and Sadlo, Filip
- pages
- 3 pages
- publisher
- Eurographics - European Association for Computer Graphics
- conference name
- EuroVis 2016, The 18th EG/VGTC Conference on Visualization
- conference location
- Groningen, Netherlands
- conference dates
- 2016-06-06 - 2016-06-10
- external identifiers
-
- scopus:85096283249
- ISBN
- 978-3-03868-015-4
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- c13b1378-ff2d-457d-86c3-a19f55180968
- alternative location
- http://diglib.eg.org/handle/10.2312/eurp20161139
- date added to LUP
- 2016-06-14 16:38:17
- date last changed
- 2022-04-10 17:10:51
@inproceedings{c13b1378-ff2d-457d-86c3-a19f55180968,
abstract = {{The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. 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.}},
author = {{Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus}},
booktitle = {{EuroVis Posters 2016}},
editor = {{Isenberg, Tobias and Sadlo, Filip}},
isbn = {{978-3-03868-015-4}},
language = {{eng}},
month = {{04}},
pages = {{49--51}},
publisher = {{Eurographics - European Association for Computer Graphics}},
title = {{Visual Analysis of Text Annotations for Stance Classification with ALVA}},
url = {{http://diglib.eg.org/handle/10.2312/eurp20161139}},
year = {{2016}},
}