Exploring Unsupervised Word Representations Models and Neural Networks for Informal Multilingual Text Against Covid-19 Social Media Content

摘要: The Covid-19 pandemic has catalyzed a marked upsurge in multilingual social media communication, distinctly marked by the fusion of dialectical Arabic with code-switched Latin scripts, primarily French and English. This linguistic phenomenon, primarily manifesting during the spread of the pandemic, poses intricate challenges for automated sentiment analysis due to the scarcity of appropriate training resources for such linguistically diverse contexts. Our research undertakes a systematic examination of this informal, multilingual textual production. Deviating from previous methodologies that predominantly relied on deep learning techniques supplemented with hand-crafted features, our study adopts an approach without any preprocessing steps. We rigorously evaluate a range of unsupervised word representation models, including word2vec, BERT, and M-BERT, and explore the application of deep learning …