Visual Analysis of Text Annotations for Stance Classification with ALVA
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:eurp.20161139,
booktitle = {EuroVis 2016 - Posters},
editor = {Tobias Isenberg and Filip Sadlo},
title = {{Visual Analysis of Text Annotations for Stance Classification with ALVA}},
author = {Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-015-4},
DOI = {10.2312/eurp.20161139}
}