Domain-Aware Sentiment Classification with GRUs and CNNs.

In this paper, we describe a deep neural network architecture for domain-aware sentiment classification task with the purpose of the sentiment classification of product reviews in different domains and evaluating nine pre-trained embeddings provided by the semantic sentiment classification challenge at the 15th Extended Semantic Web Conference. The proposed approach combines the domain and the sequence of word embeddings of the summary or text of each review for Gated Recurrent Units (GRUs) to produce the corresponding sequence of embeddings by being aware of the domain and previous words. Afterwards, it extracts local features using Convolutional Neural Networks (CNNs) from the output of the GRU layer. The two sets of local features extracted from the domain-aware summary and text of a review are concatenated into a single vector, and are used for classifying the sentiment of a review. Our approach obtained 0.9643 F1-score on the test set and achieved the 1st place in the first task of the Semantic Sentiment Analysis Challenge at the 15th Extended Semantic Web Conference.