MultiEmo: Language-Agnostic Sentiment Analysis

We developed and validated a language-agnostic method for sentiment analysis. Cross-language experiments carried out on the new MultiEmo dataset with texts in 11 languages proved that LaBSE embeddings with an additional attention layer implemented in the BiLSTM architecture outperformed other method...

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Bibliographic Details
Published inComputational Science – ICCS 2022 pp. 72 - 79
Main Authors Miłkowski, Piotr, Gruza, Marcin, Kazienko, Przemysław, Szołomicka, Joanna, Woźniak, Stanisław, Kocoń, Jan
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
BiLSTM
Cross-language NLP
LaBSE
Language-agnostic representation
LASER
MultiEmo
Opinion mining
Sentiment analysis
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Summary:We developed and validated a language-agnostic method for sentiment analysis. Cross-language experiments carried out on the new MultiEmo dataset with texts in 11 languages proved that LaBSE embeddings with an additional attention layer implemented in the BiLSTM architecture outperformed other methods in most cases.
Bibliography:This work was partially supported by the National Science Centre, Poland, project no. 2020/37/B/ST6/03806; by the statutory funds of the Department of Artificial Intelligence, Wroclaw University of Science and Technology; by the European Regional Development Fund as a part of the 2014-2020 Smart Growth Operational Programme, CLARIN - Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
ISBN:9783031087530
3031087534
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-031-08754-7_10