Aspect-level sentiment analysis aims to provide complete and detailed view of sentiment analysis from different aspects. Existing solutions usually adopt a two-staged approach：first detecting aspect category in a document, then categorizing the polarity of opinion expressions for detected aspect(s). Inevitably, such methods lead to error accumulation. Moreover, aspect detection and aspect-level sentiment classification are highly correlated with each other. The key issue here is how to perform aspect detection and aspect-level sentiment classification jointly, and effectively. In this paper, we propose the aspect-level sentiment capsules model (AS-Capsules), which is capable of performing aspect detection and sentiment classification simultaneously, in a joint manner. AS-Capsules utilizes the correlation between aspect and sentiment through shared components including capsule embedding, shared encoders, and shared attentions. AS-Capsules is also capable of communicating with different capsules through a shared Recurrent Neural Network (RNN). More importantly, AS-Capsules model does not require any linguistic knowledge as additional input. Instead, through the attention mechanism, this model is able to attend aspect related words and sentiment words corresponding to different aspect(s). Experiments show that the AS-Capsules model achieves state-of-the-art performances on a benchmark dataset for aspect-level sentiment analysis.
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