Sentiment Analysis with Contextual Embeddings and Self-Attention

Katarzyna Biesialska; Magdalena Biesialska; Henryk Rybinski

doi:10.1007/978-3-030-59491-6_4

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arXiv · 2003.05574

Sentiment Analysis with Contextual Embeddings and Self-Attention

Katarzyna Biesialska·Magdalena Biesialska·Henryk Rybinski

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Abstract

In natural language the intended meaning of a word or phrase is often implicit and depends on the context. In this work, we propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism. The experimental results for three languages, including morphologically rich Polish and German, show that our model is comparable to or even outperforms state-of-the-art models. In all cases the superiority of models leveraging contextual embeddings is demonstrated. Finally, this work is intended as a step towards introducing a universal, multilingual sentiment classifier.

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Katarzyna Biesialska, Magdalena Biesialska, Henryk Rybinski. 2020-10-05. Sentiment Analysis with Contextual Embeddings and Self-Attention. https://doi.org/10.1007/978-3-030-59491-6_4

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