Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has placed emphasis on code-switching text. The challenges of this task include the exploration both monolingual and bilingual information of each post and capturing the informative words from the code-switching context. To address these challenges, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects informative words qualitatively.
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