Knowledge-aided Dual-channel CNN (KDCNN)

KDCNN is proposed to utilize external knowledge for sentiment analysis to improve CNN’s performance on sentimental classification task. The external knowledge includes positive and negative sentimental lexicons, negation words, and intensity words. KDCNN can not only improve the precision of sentiment classification but also has less trainable model parameters and less reliance on large amount of training data, which can reduce the cost of training. This report explains KDCNN's structure and the related experiments in detail. A comparison and analysis of different neural models' performance are also included in the report.