Deep Memory Network with Auxiliary Sequences for Chinese Implied Sentiment Analysis

Abstract

Sentiment analysis is a hot topic and has various application scenarios. The polarity recognition of implied sentiment in a sentence can be achieved by the way of statistic and prediction. However, the polarity of sentiment is influenced by funny, humorous, and ironic Internet cultures, therefore it is hard to be verified. In this paper, we use a deep memory network with the auxiliary sequence to obtain the text feature vectors. Then the Emoji set and the special word set from the internet are imported, which are combined with the formal text feature vectors to form the classification feature vectors. At last a binary classifier is designed to get the final polarity prediction. Besides, an incremental online learning method with feedback adjustment is introduced to update the Emoji set and the special word set. Experiment results show that, on the IMDB datasets the prediction accuracy is about 85% and on the Chinese implied sentiment evaluation datasets the prediction accuracy is about 96%, which prove the effectiveness of the model.