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Active Learning and Visual Analytics for Stance Classification with ALVA

Active Learning and Visual Analytics for Stance Classification with ALVA Active Learning and Visual Analytics for Stance Classification with ALVA KOSTIANTYN KUCHER, Linnaeus University CARITA PARADIS, Lund University MAGNUS SAHLGREN, Swedish Institute of Computer Science and Gavagai AB ANDREAS KERREN, Linnaeus University The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers ™ attitudes toward their own and other people ™s utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA ™s interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Active Learning and Visual Analytics for Stance Classification with ALVA

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/3132169
Publisher site
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Abstract

Active Learning and Visual Analytics for Stance Classification with ALVA KOSTIANTYN KUCHER, Linnaeus University CARITA PARADIS, Lund University MAGNUS SAHLGREN, Swedish Institute of Computer Science and Gavagai AB ANDREAS KERREN, Linnaeus University The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers ™ attitudes toward their own and other people ™s utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA ™s interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Oct 9, 2017

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