Aspect level sentiment classification is a finegrained sentiment analysis task, compared to the sentence level classification. A sentence usually contains one or more aspects. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various methods for generating aspectspecific sentence representations. However, these studies handle each aspect of a sentence separately. In this paper, we argue that multiple aspects of a sentence are usually orthogonal based on the observation that different aspects concentrate on different parts of the sentence. To force the orthogonality among aspects, we propose constrained attention networks (CAN) for multi-aspect sentiment analysis, which handles multiple aspects of a sentence simultaneously. Experimental results on two public datasets demonstrate the effectiveness of our approach. We also extend our approach to multi-task settings, outperforming the state-of-the-arts significantly. * This work was done when Mengting Hu was a research intern at IBM Research -China. The tastes are great , but the service is dreadful .
REF propose constrained attention networks (CAN), which extends AT-LSTM to multi-task settings and introduces orthogonal and sparse regularizations to constrain the attention weight allocation.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
ArXiv
ArXiv
Computer Science