Research on text sentiment analysis based on CNNs and SVM

Abstract

Sentiment analysis of Internet reviews is a hot research topic in Web information mining. The traditional text sentiment analysis method is mainly based on emotion dictionary or machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolutional Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM.