Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews

Hui Du, Xueke Xu, Xueqi Cheng, Dayong Wu, Yue Liu, Zhihua Yu

Abstract: Recently, Deep Convolutional Neural Networks (CNNs) have been widely applied to sentiment analysis of short texts. Naturally, word embedding techniques are used to learn continuous word representations for constructing sentence matrix as input to CNN. As for sentiment analysis of customer reviews, we argue that it is problematic to learn a single representation for a word while ignoring sentiment information and the discussed aspects. In this poster, we propose a novel word embedding model to learn sentimental word embedding given specific aspects by modeling both sentiment and syntactic context under the specific aspects. We apply our method as input to CNN for sentiment analysis in multiple domains. Experiments show that the CNN based on the proposed model can consistently achieve superior performance compared to CNN based on traditional word embedding method.