Developing a Deep Neural Networks Method based on Sentiment Embeddings for Sentiment Analysis

Abstract

Most of the existing learning method based on word vector, therefore, only the grammatical context modeling, and ignored the word of emotional information, can't very well solve the emotion classification task. In this paper, for sentiment analysis, an improved Deep Neural Networks method based on sentiment embeddings is proposed. Firstly, two strategies are generated for emotional coding of sentence polarity and contextual word information, and then emotional information in the text is combined with the contextual words of the current word to enhance the emotional information of the word vector. And then, an active learning method under deep confidence network is constructed by the adaptive learning method of deep confidence network, which improves the text classification ability and effectively solve the problem of long text sentiment classification sample selection in semi-supervised learning method. The validity of the proposed model is verified by quantitative experiments on word levels in different language and different dictionary data sets. The correct rate of sentiment classification has been further improved, which further confirms the application value of the model.