Visual Analysis of Text Annotations for Stance Classification with ALVA

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Bibtex

@inproceedings{c13b1378ff2d457d86c3a19f55180968,
title = "Visual Analysis of Text Annotations for Stance Classification with ALVA",
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{\textquoteright} feelings and attitudes towards their own and other people{\textquoteright}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 = "Kostiantyn Kucher and Andreas Kerren and Carita Paradis and Magnus Sahlgren",
year = "2016",
month = apr,
day = "28",
language = "English",
pages = "49--51",
editor = "Tobias Isenberg and Sadlo, {Filip }",
booktitle = "EuroVis Posters 2016",
publisher = "Eurographics - European Association for Computer Graphics",
address = "Switzerland",
note = "EuroVis 2016, The 18th EG/VGTC Conference on Visualization ; Conference date: 06-06-2016 Through 10-06-2016",

}