HPCC - YNU at S em E val-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text (2020)

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 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.

## introduction sentiment analysis @xcite 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 @xcite 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 @xcite , 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 monolingual resources as the input features. then, these features were be put into deep neural networks. among the deep learning approaches, sub-word level representations in convolutional neural network (cnn) @xcite based on the long short-term memory (lstm) @xcite architecture was presented by @xcite . others used features such as glove @xcite word embeddings with 300 dimensions. furthermore, they trained an ensemble model that contains a linear support vector machine (svm), logistic regression and random forest to detect the sentiments. in this paper, we propose a vector gating mechanism to combine multiple monolingual word-level and char-level embeddings in a novel architecture. the char-bilstm layer is used to capture the characterlevel information and the word feature representation is generated by bilingual embedding. then, the combined representation is processed using bilstm based attention. in our model, the bilstm is used to capture the long-term dependencies between bilingual word sequences and character sequences. the gating mechanism can effectively combine character level and word level information, namely, the proposed model can precisely capture the emotional expression of code-mixed text. our submission ranked fifth in spanglish and 19th in hinglish. the rest of this paper is organized as follows. section 2 describes the overall structure of our model and the gating mechanism. then, the comparative experimental results are presented in section 3. finally, the conclusions are finally drawn in section 4. ## bilingual vector gating model figure 1 presents the overall architecture of our model. it uses preprocessed text as input to the model. in addition to taking word embeddings as the input, we also separate the words into characters and feed the character embeddings to the model. the word and character level representations are composed of bilingual pre-trained word vectors and the output of the char-bilstm layer. then, the character-and word-level features are combined using the vector gating mechanism. finally, a bi-directional lstm (bilstm) with attention is used to calculate the gating vector to obtain the final result.