Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis

Representing words as real-value vectors and use them as input in deep neural networks is an effective approach in many natural language processing tasks. Currently, some studies use a lower-level representation which is character-based vectors. This paper addresses on how to integrate different representations of input for the problem of aspect-based sentiment analysis. We will propose a joint model of multiple Convolutional Neural Networks (CNNs) in which each individual representation of the input is handled by one CNN. In this work we focus on three kinds of representation including word embeddings from the two methods (Word2Vec and GloVe) and the one-hot character vectors. Our experimental results demonstrate that the proposed model can achieve state-of the-art performance in aspect category detection and aspect sentiment classification tasks.