Abstract
Text Sentiment Analysis (TSA) is becoming a hot area of research in the field of Natural Language Processing (NLP). There are many researches on English text sentiment analysis, while the Chinese do not attract sufficient attention. In this paper, we provide a novel deep learning model called Bilinear Character-Word Convolutional Neural Networks (BCWCNN) to deal with Chinese text sentiment analysis task. Our model represent a Chinese sentence as a bilinear combination of features learned from two-stream CNN models, which receives character-level and word-level embedding features as input, respectively. Experiments conducted on Chinese text sentiment corpus demonstrate that the proposed architecture significantly improve the performance of this task compared to other existing architectures.
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