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        <title>Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages</title>
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    <name type="personal">
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            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1231</identifier>
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%0 Conference Proceedings
%T Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
%A Barnes, Jeremy
%A Klinger, Roman
%A Schulte im Walde, Sabine
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F barnes-etal-2018-bilingual
%X Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.
%R 10.18653/v1/P18-1231
%U https://aclanthology.org/P18-1231
%U https://doi.org/10.18653/v1/P18-1231
%P 2483-2493

Markdown (Informal)

[Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages](https://aclanthology.org/P18-1231) (Barnes et al., ACL 2018)

• Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages (Barnes et al., ACL 2018)

ACL

• Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde. 2018. Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2483–2493, Melbourne, Australia. Association for Computational Linguistics.