Ensemble of Neural Networks with Sentiment Words Translation for Code-Switching Emotion Detection

Abstract

Emotion detection in code-switching texts aims to identify the emotion labels of text which contains more than one language. The difficulties of this task include problems in bridging the gap between languages and capturing crucial semantic information for classification. To address these issues, we propose an ensemble model with sentiment words translation to build a powerful system. Our system first constructs an English-Chinese sentiment dictionary to make a connection between two languages. Afterwards, we separately train several models include CNN, RCNN and Attention based LSTM model. Then combine their classification results to improve the performance. The experiment result shows that our method has a good effect and achieves the second place among nineteen systems.