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Active Learning and Visual Analytics for Stance Classification with ALVA
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV). (ISOVIS)ORCID-id: 0000-0002-1907-7820
Lund University.ORCID-id: 0000-0002-7240-9003
Swedish Research Institute (RISE SICS).
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV). (ISOVIS)ORCID-id: 0000-0002-0519-2537
2017 (Engelska)Ingår i: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, nr 3, artikel-id 14Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine learning methods create an opportunity to gain insight into the speakers' attitudes towards their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. In order 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 in order to select suitable candidate utterances for manual annotation. 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 stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. 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.

Ort, förlag, år, upplaga, sidor
New York, NY, USA: ACM Publications, 2017. Vol. 7, nr 3, artikel-id 14
Nyckelord [en]
visualization, stance visualization, active learning, text visualization, sentiment visualization, annotation, visual analytics, sentiment analysis, stance analysis, NLP, text analytics
Nationell ämneskategori
Datavetenskap (datalogi) Språkteknologi (språkvetenskaplig databehandling)
Forskningsämne
Datavetenskap, Informations- och programvisualisering
Identifikatorer
URN: urn:nbn:se:lnu:diva-67173DOI: 10.1145/3132169ISI: 000414322200005Scopus ID: 2-s2.0-85032958347OAI: oai:DiVA.org:lnu-67173DiVA, id: diva2:1129673
Projekt
StaViCTA
Forskningsfinansiär
Vetenskapsrådet, 2012-5659Tillgänglig från: 2017-08-05 Skapad: 2017-08-05 Senast uppdaterad: 2019-08-29Bibliografiskt granskad

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Kucher, KostiantynKerren, Andreas

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Kucher, KostiantynParadis, CaritaKerren, Andreas
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Institutionen för datavetenskap (DV)
Datavetenskap (datalogi)Språkteknologi (språkvetenskaplig databehandling)

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