A hybrid method for bilingual text sentiment classification based on deep learning

Abstract

Text sentiment classification has occupied a pivotal position in sentiment analysis research, it offers important opinion mining functions. Nowadays, with explosion of information, many researchers are focusing on sentiment classification research on massive amounts of data. However, the traditional machine learning methods cannot acquire text semantic information and most research achievements are about single language, in this paper, a hybrid method which integrates the deep learning features and shallow learning features is proposed. The hybrid method can not only realize single language text sentiment classification but realize bilingual text sentiment classification as well. Models such as recurrent neural networks (RNNs) with long short term memory(LSTM), Naive Bayes Support Vector Machine (NB-SVM), word vectors and bag-of-words are explored. Firstly, these models are studied separately in sentiment classification task. The paper then integrates the above methods as a whole to complete the task. Different combination strategies are discussed regarding the contribution of each method. The experiments show that the accuracy can reach 89% and the hybrid method performs much better than any other method individually. The proposed method achieves a performance close to the state-of-the-art methods based on the had-engineered features. What's more, the hybrid model can learn more linguistic phenomena with the growth of the accuracy of emotional tendency discrimination when more background knowledge is available.