A novel sentiment classification model based on online learning

Memory limitation and slow training speed are two important problems in sentiment analysis. In this paper, we propose a sentiment classification model based on online learning to improve the training speed of the sentiment classification. First, combining the adaptive adjustment of learning rate of the Adadelta algorithm and the characteristics of avoid frequent jitter of Adam algorithm in the later stage of training, we present a novel Adamdelta algorithm. It solves the problem that learning rate of traditional follow the regularized leader (FTRL)-Proximal online learning algorithm will disappear with the increase of training times. Moreover, we gain an optimized logistic regression (LR) model and use it to the sentiment classification of online learning. Finally, we compare the proposed algorithm with five similar models with the experimental data of the IMDb movie review dataset. Experimental results show that the improved algorithm has better classification effect and can effectively improve the precision and recall of the classifier.