Extracting different emotions of different aspects in user comments is a fundamental task of sentiment analysis. For example, “I like apple, but hate banana.”, for aspect apple, the polarity is positive while for banana is negative. So that aspect-level sentiment classification has become pervasive in recent years. In this paper, we present a new framework for aspect-level sentiment classification by attention-based convolutional neural networks. The attention mechanism can focus on different aspects in a sentence, and extract different polarities. The experimental results in SemEval 2014 dataset show that our model achieves state-of-the-art performance on aspect-level sentiment classification.
Attention-based CNNs for Aspect-level Sentiment Classification
Published: 31 December 2016 by MDPI AG in Proceedings of MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition
MDPI AG, 10.3390/mol2net-02-03853