Sentiment commonsense induced sequential neural networks for sentiment classification

Abstract

Although neural networks achieve promising performance in sentence level sentiment classification, most of them are not aware of sentiment commonsense, such as sentiment polarity tags (Positive or Negative) for words, which explicitly determine the sentiment of the sentence in most cases. In this paper, we propose an auxiliary tagging task to integrate sentiment commonsense into sequential neural networks (such as LSTM). We employ the advantage of multitask learning to achieve two goals simultaneously: 1) the sequential learning task accounts for incorporating the semantic information of the surrounding words; 2) the word tagging task ensures the sequential representation still retains the corresponding word tagging information. Besides, considering the most direct way to introduce sentiment information into models as additional knowledge, we further incorporate the additional knowledge enhancing tagging task model to strengthen the effect of sentiment commonsense. We prove the effectiveness of the sentiment commonsense by extensive experiments. The results show that our models exhibit consistent superiority over competitors on three real-word datasets. Specifically, we obtain an accuracy of 55.2%, which is a new state-of-the-art for SST-fine dataset.