Sentiment Analysis Model of Imbalanced Comment Texts Based on BiLSTM

Abstract

Abstract This paper tries to improve the performance of imbalanced comment texts sentiment analysis by combining deep learning and class imbalanced learning methods. A sentiment analysis method for imbalanced comment texts based on BiLSTM framework is proposed. For the case of more negative samples than positive samples, when the degree of imbalance is low, the minority class samples are Adaptive Synthetic Sampling, and the CNN-BiLSTM model is proposed to realize sentiment classification by constructing Sigmoid. When the degree of imbalance is high, the samples of majority class are sampled multiple times until the original dataset is divided into multiple low imbalance datasets. Then, multiple groups of equalization Adaptive Synthetic Sampling is carried out for the samples of minority class, and BiLSTM model is learned for each group of training data respectively. Finally, Ensemble learning is adopted to obtain the final sentiment classification results. Experimental results show that this paper method is superior to the traditional imbalanced comment texts sentiment analysis method.