Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2018
DOI: 10.18653/v1/p18-1231
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Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages

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 b… Show more

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Cited by 44 publications
(43 citation statements)
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“…There are also approaches to learn sentimental embeddings in the bilingual space without any sentiment resources in the target language. Barnes et al (2018) jointly minimized an alignment objective based on a seed dictionary, and a classification objective based on the sentiment corpus. Its performance is compared to our method in Section 4.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…There are also approaches to learn sentimental embeddings in the bilingual space without any sentiment resources in the target language. Barnes et al (2018) jointly minimized an alignment objective based on a seed dictionary, and a classification objective based on the sentiment corpus. Its performance is compared to our method in Section 4.…”
Section: Related Workmentioning
confidence: 99%
“…We use the multilingual sentiment dataset provided by (Barnes et al, 2018). It contains annotated hotel reviews in English (EN), Spanish (ES), Catalan (CA) and Basque (EU).…”
Section: Datasetsmentioning
confidence: 99%
“…Machine learning methods were found to usually outperform rule-based approaches based on look-ups in dictionaries such as LIWC. Again, most annotated resources are English, but state of the art approaches based on multilingual em-beddings allow transferring models between languages (Barnes et al, 2018a).…”
Section: Linguisticmentioning
confidence: 99%
“…Another approach for obtaining morphological analyses for languages without a morphological analyzer is based on transfer learning, which has become a widespread approach in NLP and related disciplines rather recently (Yarowsky et al, 2001;Faruqui and Kumar, 2015;Johnson et al, 2017;Barnes et al, 2018). The general idea is to train a supervised machine learning model that predicts analyses of word forms in a target language using gold-standard analyses that exist in other related languages.…”
Section: Introductionmentioning
confidence: 99%