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Effective representation of a text is critical for various natural languageprocessing tasks. For the particular task of Chinese sentiment analysis, it isimportant to understand and choose an effective representation of a text fromdifferent forms of Chinese representations such as word, character and pinyin.This paper presents a systematic study of the effect of these representationsfor Chinese sentiment analysis by proposing a multi-channel convolutionalneural network (MCCNN), where each channel corresponds to a representation.Experimental results show that: (1) Word wins on the dataset of low OOV ratewhile character wins otherwise; (2) Using these representations in combinationgenerally improves the performance; (3) The representations based on MCCNNoutperform conventional ngram features using SVM; (4) The proposed MCCNN modelachieves the competitive performance against the state-of-the-art modelfastText for Chinese sentiment analysis.

Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network