Sentiment Analysis Based on Multiple Features Convolutional Neural Networks

In the task of Micro-Blog sentiment analysis, the deep neural-based models are difficult to make full use of the sentiment information. To solve this problem, a Multiple Features Convolutional Neural Networks (MF-CNN) model is proposed. The emotional information in sentences is effectively utilized by combining the abstract features of words and two kinds of calculation methods of neural model input matrix, and then the sentiment classification result is optimized. The sentiment analysis is carried out on COAE2014 and Micro-Blog text data set, and the results show that the classification effect of MF-CNN model is better than that of traditional classifier and deep Convolutional Neural Network (CNN) model.