Identifying Implicit Polarity of Events by Using an Attention-Based Neural Network Model - Chunli Xiang; Yafeng Ren; Donghong Ji (IEEE Access, 2019)
Sentiment analysis has attracted extensive attention in recent years. Existing work mainly focuses on sentiment classification task in which a text or sentence usually contains sentiment word to express subjective feeling. However, little research is proposed for identifying implicit polarity of a text. Here, implicit polarity of a text means that the text does not contain sentiment words but still express a positive or negative sentiment. To address this issue, we propose an attention-based neural network model to identify implicit polarity of events. In particular, the model first learns the sentence representation by recurrent neural network with gated recurrent unit. Then multiple hops attention mechanism is used for capturing multiple aspects closely related to sentiment polarity and the event type. Experimental results on SemEval 2015 dataset show the effectiveness of the proposed model, outperforming the previous systems and strong neural baselines.