Advanced

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 In EuroVis Posters 2016 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
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
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
2016-06-22 13:32:25
@misc{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},
  editor       = {Isenberg, Tobias and Sadlo, Filip },
  language     = {eng},
  month        = {04},
  pages        = {49--51},
  publisher    = {ARRAY(0xaa30a60)},
  series       = {EuroVis Posters 2016},
  title        = {Visual Analysis of Text Annotations for Stance Classification with ALVA},
  year         = {2016},
}