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Visual Analysis of Text Annotations for Stance Classification with ALVA

In English

Författare

Redaktör

  • Tobias Isenberg
  • Filip Sadlo

Summary, in English

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.

Avdelning/ar

  • Engelska
  • Language, Cognition and Discourse@Lund (LCD@L)

Publiceringsår

2016-04-28

Språk

Engelska

Sidor

49-51

Publikation/Tidskrift/Serie

EuroVis Posters 2016

Dokumenttyp

Konferensbidrag

Förlag

Eurographics - European Association for Computer Graphics

Ämne

  • General Language Studies and Linguistics
  • Human Computer Interaction
  • Language Technology (Computational Linguistics)

Conference name

EuroVis 2016, The 18th EG/VGTC Conference on Visualization

Conference date

2016-06-06 - 2016-06-10

Conference place

Groningen, Netherlands

Status

Published

Projekt

  • StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics

Forskningsgrupp

  • Language, Cognition and Discourse@Lund (LCD@L)

ISBN/ISSN/Övrigt

  • ISBN: 978-3-03868-015-4