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Twenty-four studies on twenty-three distinct languages and eleven social media illustrate the steady interest in deep learning approaches for multilingual sentiment analysis of social media. We improve over previous reviews with wider coverage from 2017 to 2020 as well as a study focused on the underlying ideas and commonalities behind the different solutions to achieve multilingual sentiment analysis. Interesting findings of our research are (i) the shift of research interest to cross-lingual and code-switching approaches, (ii) the apparent stagnation of the less complex architectures derived from a backbone featuring an embedding layer, a feature extractor based on a single CNN or LSTM and a classifier, (iii) the lack of approaches tackling multilingual aspect-based sentiment analysis through deep learning, and, surprisingly, (iv) the lack of more complex architectures such as the transformers-based, despite results suggest the more difficult tasks requires more elaborated architectures. • Review of applications of Deep Learning to tackle Multilingual Sentiment Analysis. • Fast-growing interest in this field, 24 related papers since 2017 to 2020. • Coverage of 23 different languages and 11 social media data or corpus. • Mixed performance, but word embeddings and CNN or LSTM as trending choices. • Embeddings>feature extractor>classifier, prevailing architecture except for aspect SA. [ABSTRACT FROM AUTHOR]