Sentiment analysis has immense implications in modern businesses through user-feedback mining. Large product-based enterprises like Samsung and Apple make crucial business decisions based on the large quantity of user reviews and suggestions available in different e-commerce websites and social media platforms like Amazon and Facebook. Sentiment analysis caters to these needs by summarizing user sentiment behind a particular object. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. Our method outperforms the state of the art by 1.6% on average in two distinct domains.
展开▼
