  Sentiment analysis is a process widely used in opinion mining campaigns
conducted today. This phenomenon presents applications in a variety of fields,
especially in collecting information related to the attitude or satisfaction of
users concerning a particular subject. However, the task of managing such a
process becomes noticeably more difficult when it is applied in cultures that
tend to combine two languages in order to express ideas and thoughts. By
interleaving words from two languages, the user can express with ease, but at
the cost of making the text far less intelligible for those who are not
familiar with this technique, but also for standard opinion mining algorithms.
In this paper, we describe the systems developed by our team for SemEval-2020
Task 9 that aims to cover two well-known code-mixed languages: Hindi-English
and Spanish-English.
  We intend to solve this issue by introducing a solution that takes advantage
of several neural network approaches, as well as pre-trained word embeddings.
Our approach (multlingual BERT) achieves promising performance on the
Hindi-English task, with an average F1-score of 0.6850, registered on the
competition leaderboard, ranking our team 16th out of 62 participants. For the
Spanish-English task, we obtained an average F1-score of 0.7064 ranking our
team 17th out of 29 participants by using another multilingual
Transformer-based model, XLM-RoBERTa.
