  Effective representation of a text is critical for various natural language
processing tasks. For the particular task of Chinese sentiment analysis, it is
important to understand and choose an effective representation of a text from
different forms of Chinese representations such as word, character and pinyin.
This paper presents a systematic study of the effect of these representations
for Chinese sentiment analysis by proposing a multi-channel convolutional
neural network (MCCNN), where each channel corresponds to a representation.
Experimental results show that: (1) Word wins on the dataset of low OOV rate
while character wins otherwise; (2) Using these representations in combination
generally improves the performance; (3) The representations based on MCCNN
outperform conventional ngram features using SVM; (4) The proposed MCCNN model
achieves the competitive performance against the state-of-the-art model
fastText for Chinese sentiment analysis.
