Home » MOL2NET » Section 11: SUIWML01: International Workshop on Machine Learning in Biomedicine, Soochow, 2016 » Paper

[] Attention-based CNNs for Aspect-level Sentiment Classification

Soochow university
31 December 2016
356 views
0/5 rated ( 0 ratings )

Abstract

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.

Cite this article as

jin, x. Attention-based CNNs for Aspect-level Sentiment Classification. In Proceedings of the MOL2NET, International Conference on Multidisciplinary Sciences, 25 December 2016–25 January 2017; Sciforum Electronic Conference Series, Vol. 2, 2016 ; doi:10.3390/mol2net-02-03853

Presentation

Comments on Attention-based CNNs for Aspect-level Sentiment Classification