Visual Analysis of Text Annotations for Stance Classification with ALVA
Visual Analysis of Text Annotations for Stance Classification with ALVA
复制标题
使用 ALVA 对立场分类文本注释进行可视化分析
DOI：
10.2312/eurp.20161139
复制
发表时间：
2016
影响因子：
1.9
通讯作者：
Magnus Sahlgren
中科院分区：
文献类型：
--
作者：
Kostiantyn Kucher;A. Kerren;C. Paradis;Magnus Sahlgren
使用自然语言处理和机器学习方法对文本数据中的立场进行自动检测和分类，创造了一个机会来了解作者对自己和他人话语的感受和态度。然而，这项任务提出了与训练数据收集以及实际分类器训练相关的多个挑战。为了方便训练的立场分类器的过程中，我们提出了一个可视化的分析方法称为阿尔瓦的文本数据注释和可视化。我们的方法支持注释过程管理，并为注释者提供了一个干净的用户界面，用于标记具有多个立场类别的话语。分析师提供了一个可视化的立场注释，这有利于分析所使用的注释类别。阿尔瓦已经被我们在语言学和计算语言学领域的专家使用，以提高对立场现象的理解，并为社交媒体监控等应用构建立场分类器。
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.