  It is fairly common to use code-mixing on a social media platform to express
opinions and emotions in multilingual societies. The purpose of this task is to
detect the sentiment of code-mixed social media text. Code-mixed text poses a
great challenge for the traditional NLP system, which currently uses
monolingual resources to deal with the problem of multilingual mixing. This
task has been solved in the past using lexicon lookup in respective sentiment
dictionaries and using a long short-term memory (LSTM) neural network for
monolingual resources. In this paper, we (my codalab username is kongjun)
present a system that uses a bilingual vector gating mechanism for bilingual
resources to complete the task. The model consists of two main parts: the
vector gating mechanism, which combines the character and word levels, and the
attention mechanism, which extracts the important emotional parts of the text.
The results show that the proposed system outperforms the baseline algorithm.
We achieved fifth place in Spanglish and 19th place in Hinglish.The code of
this paper is availabled at : https://github.com/JunKong5/Semveal2020-task9
