Deep Neural Networks for Multi-class Sentiment Classification

Abstract

Traditional text sentiment analysis methods often ignore context information when used in the expression of features. The position of the words in the text makes it difficult to achieve satisfactory results in semantic realization. In recent years, deep learning has obtained good results in text sentiment analysis tasks. Convolutional neural network (CNN) and Recurrent Neural Network(RNN) are two mainstream deep learning algorithms. In this paper, a deep sentiment representation model based on CNNs and long short-term memory recurrent neural network (LSTM) is proposed. The model uses two layers of CNNs to capture the partial features of the text. The model can capture more accurate partial features, after which the features are fed to the LSTM, which can capture the contextual information. Finally, we combine the improved deep learning model with a one-versus-rest training mechanism and apply it to multi-class sentiment classification. We evaluate the proposed model by conducting experiments on datasets. Experimental results demonstrate that the model we designed for multi-class sentiment analysis achieves a accuracy of 78.42% on data set D1 is better than the existing SVMs (support vector machines), CNN, LSTM and CNN-LSTM.