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

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 940–945 Barcelona, Spain (Online), December 12, 2020. 940 HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text Jun Kong, Jin Wang and Xuejie Zhang School of Information Science and Engineering Yunnan University Kunming, China Contact:{wangjin, xjzhang}@ynu.edu.cn Abstract 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 1 Introduction Sentiment analysis (Wang et al., 2018; Wang et al., 2019a) of social media data has been a hot research topic in the field of text in recent years. The emotion expressed in a phrase or sentence allows us to identify a person’s point of view. Furthermore, the sentiment analysis of social media is critical to business and the government. With the integration of multiculturalism, there are many code-mixed texts on social media platforms. Code-mixing is a phenomenon in which two or more language units are mixed in one sentence, especially in multilingual societies around the world. Code-mixing specifically refers to the use of words, phrases, clauses and other language units in different languages at the sentence level. The purpose of Sentiment Analysis for Code-Mixed Social Media Text (Patwa et al., 2020) is to analyze the sentiment of code-mixed text on social media platforms. The sentiment polarities of sentences include the following: positive, neutral and negative. Compared to monolingual sentiment analysis (Wang et al., 2019b), coded-mixed text sentiment analysis is difficult due to the following reasons: (1) the language complexity of code-mixed content is exacerbated by spelling changes, slangs and non-compliance with formal grammar; (2) traditional semantic analysis methods cannot capture the meaning of code-mixed sentences; (3) most previous studies are focused on a single language, which ignores the phenomenon of code-mixing; (4) some words with the same spelling may have completely different meanings in different languages. For this task, previous works have mostly focused on applying pre-trained word embedding on mono- lingual resources as the input features. Then, these features were be put into deep neural networks. Among …

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