UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages

Ehsaneddin Asgari, Fabienne Braune, Benjamin Roth, Christoph Ringlstetter, Mohammad Mofrad

Abstract
In this paper, we introduce UniSent universal sentiment lexica for 1000+ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of the number of covered languages, including many low resource ones. In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain. We evaluate the quality of UniSent in a subset of languages for which manually created ground truth was available, Macedonian, Czech, German, Spanish, and French. We show that the quality of UniSent is comparable to manually created sentiment resources when it is used as the sentiment seed for the task of word sentiment prediction on top of embedding representations. In addition, we show that emoticon sentiments could be reliably predicted in the Twitter domain using only UniSent and monolingual embeddings in German, Spanish, French, and Italian. With the publication of this paper, we release the UniSent sentiment lexica at http://language-lab.info/unisent.
Anthology ID:
2020.lrec-1.506
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
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, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4113–4120
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.506
DOI:
Bibkey:
Cite (ACL):
Ehsaneddin Asgari, Fabienne Braune, Benjamin Roth, Christoph Ringlstetter, and Mohammad Mofrad. 2020. UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4113–4120, Marseille, France. European Language Resources Association.
Cite (Informal):
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages (Asgari et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.506.pdf
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