Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings

Peng Liu, Cristina Marco, Jon Atle Gulla


Abstract
This paper presents a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages: Danish, Norwegian and Swedish. This method benefits from the English Sentiwordnet and a thesaurus in one of the target languages. Sentiment information from the English resource is mapped to the target languages by using machine translation and similarity measures based on sentence embeddings. A number of experiments with Scandinavian languages are performed in order to determine the best working sentence embedding algorithm for this task. A careful extrinsic evaluation on several datasets yields state-of-the-art results using a simple rule-based sentiment analysis algorithm. The resources are made freely available under an MIT License.
Anthology ID:
2022.lrec-1.301
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2816–2825
Language:
URL:
https://aclanthology.org/2022.lrec-1.301
DOI:
Bibkey:
Cite (ACL):
Peng Liu, Cristina Marco, and Jon Atle Gulla. 2022. Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2816–2825, Marseille, France. European Language Resources Association.
Cite (Informal):
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings (Liu et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.301.pdf
Data
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