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.INDEX TERMS Implicit polarity, events, neural network, attention mechanism.
Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task interactions, we present a novel lightweight joint learning paradigm that can pass task-related messages between tasks during training iterations. This is distinct from previous methods that involve multi-task learning by simple parameter sharing. Besides, a human-annotated corpus with implicit sentiment labels and event labels is scarce, which hinders practical applications of deep neural models. Therefore, we further investigate a back-translation approach to expand training instances. Experiment results on a public benchmark demonstrate the effectiveness of both the proposed multi-task architecture and data augmentation strategy.
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