A Multi-model Fusion Framework based on Deep Learning for Sentiment Classification

Abstract

With the development of the Internet, more and more data can be found on texts information. People produce texts information via writing blogs, product reviews, microblogs, film reviews and so on, which contain sentiments or opinions of the writer. User comments usually can reflect their intuitive feelings for a product. We can dig out relatively large value through sentiment analysis for these comments. Sentiment classification, as one of the most important tasks in sentiment analysis for many real world applications, is our main focus in this article. To improve the accuracy of sentiment classification, we propose a deep neural network fusion framework, which is composed of a multi-window CNN-LSTM model and a multi-window CNN-CNN model with the fusion of the probability of two models to generate final output. Experimental results instruct that our framework is quite feasible.