<div class="csl-bib-body">
<div class="csl-entry">Kolb, T. E., Sekanina, K., Kern, B. M. J., Neidhardt, J., Wissik, T., & Baumann, A. (2022). The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers. In <i>LREC 2022 Conference Proceedings</i> (pp. 4708–4716). European Language Resources Association. https://doi.org/10.34726/4101</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177094
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dc.identifier.uri
https://doi.org/10.34726/4101
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dc.description.abstract
This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.
en
dc.description.sponsorship
Stadt Wien
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dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
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dc.subject
Collaborative Resource Construction
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dc.subject
Crowdsourcing
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dc.subject
Digital Humanities
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dc.subject
Document Classification
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dc.subject
Text Categorisation
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dc.subject
Information Extraction
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dc.subject
Information Retrieval
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dc.subject
Statistical and Machine Learning Methods
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dc.subject
Tools
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dc.subject
Systems
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dc.subject
Applications
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dc.title
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Nicht kommerziell 4.0 International
de
dc.identifier.doi
10.34726/4101
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dc.contributor.affiliation
Austrian Academy of Sciences, Austria
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dc.contributor.affiliation
University of Vienna, Austria
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dc.contributor.affiliation
Austrian Academy of Sciences, Austria
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dc.contributor.affiliation
University of Vienna, Austria
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dc.relation.isbn
979-10-95546-72-6
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dc.description.startpage
4708
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dc.description.endpage
4716
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dc.relation.grantno
MA 7 – 737909/19
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dcterms.dateSubmitted
2022-06
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
LREC 2022 Conference Proceedings
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tuw.peerreviewed
true
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tuw.relation.publisher
European Language Resources Association
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tuw.relation.publisherplace
Marseille, France
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tuw.project.title
Dynamische Sentimentanalyse als emotionaler Kompass für die digitale Medienlandschaft
