Deep learning model for sentiment analysis in multi-lingual corpus

Abstract

While most text classification studies focus on monolingual documents, in this article, we propose an empirical study of poly-languages text sentiment classification model, based on Convolutional Networks ConvNets. The novel approach consists on feeding the deep neural network with one input text source composed by reviews all written in different languages, without any code-switching indication, or language translation. We construct a multi-lingual opinion corpus combining three languages: English French and Greek all from Restaurants Reviews. Despite the limited contextual information due to relatively compact text content, no prior knowledge is used. The neural networks exploit n-gram level information, and the experimental results achieve high accuracy for sentiment polarity prediction, both positive and negative, which lead us to deduce that ConvNets features extraction is language independent.