@inproceedings{galeshchuk-etal-2019-sentiment,
title = "Sentiment Analysis for Multilingual Corpora",
author = "Galeshchuk, Svitlana and
Qiu, Ju and
Jourdan, Julien",
editor = "Erjavec, Toma\v z and
Marci\'nczuk, Micha\l and
Nakov, Preslav and
Piskorski, Jakub and
Pivovarova, Lidia and
\v Snajder, Jan and
Steinberger, Josef and
Yangarber, Roman",
booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3717/",
doi = "10.18653/v1/W19-3717",
pages = "120--125",
abstract = "The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google's Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86\% of classification accuracy on the out-of-sample data."
}
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<abstract>The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google’s Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86% of classification accuracy on the out-of-sample data.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis for Multilingual Corpora
%A Galeshchuk, Svitlana
%A Qiu, Ju
%A Jourdan, Julien
%Y Erjavec, Toma\v z.
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F galeshchuk-etal-2019-sentiment
%X The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google’s Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86% of classification accuracy on the out-of-sample data.
%R 10.18653/v1/W19-3717
%U https://aclanthology.org/W19-3717/
%U https://doi.org/10.18653/v1/W19-3717
%P 120-125
Markdown (Informal)
[Sentiment Analysis for Multilingual Corpora](https://aclanthology.org/W19-3717/) (Galeshchuk et al., BSNLP 2019)
ACL
- Svitlana Galeshchuk, Ju Qiu, and Julien Jourdan. 2019. Sentiment Analysis for Multilingual Corpora. In Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pages 120–125, Florence, Italy. Association for Computational Linguistics.