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It is fairly common to use code-mixing on a social media platform to expressopinions and emotions in multilingual societies. The purpose of this task is todetect the sentiment of code-mixed social media text. Code-mixed text poses agreat challenge for the traditional NLP system, which currently usesmonolingual resources to deal with the problem of multilingual mixing. Thistask has been solved in the past using lexicon lookup in respective sentimentdictionaries and using a long short-term memory (LSTM) neural network formonolingual resources. In this paper, we (my codalab username is kongjun)present a system that uses a bilingual vector gating mechanism for bilingualresources to complete the task. The model consists of two main parts: thevector gating mechanism, which combines the character and word levels, and theattention 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 ofthis paper is availabled at : https://github.com/JunKong5/Semveal2020-task9

HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text