Deep learning and multilingual sentiment analysis on social media data: An overview

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Current studies on multilingual sentiment analysis of social media show a shift in research interest towards cross-lingual and code-switching approaches, while traditional simple architectures seem to be stagnating. Deep learning methods lack approaches for handling multilingual aspect-based sentiment analysis as well as the application of more complex architectures, despite indicating that more challenging tasks require elaborate architectures.