An Sentiment Analysis Model of Online Product Reviews Based on Deep Learning

In order to improve the accuracy of sentiment classification of online product reviews, a model for sentiment analysis of unbalanced reviews is proposed. Firstly, the LDA model is used to balance the original review text set, and then the word vector model and convolution neural network are combined to construct the review text vectorization feature extraction process to obtain the word feature vector, which is used as the input matrix of the balanced review set. Finally, the BiLSTM algorithm is used for sentiment classification to obtain product reviews of positive and negative sentiment categories. The results show that LDA sampling unbalance processing method is a high accuracy unbalanced text processing method. BiLSTM algorithm has better effect of sentiment classification than other deep learning algorithms. CNN-BiLSTM model based on LDA unbalance processing obtains the optimal model performance evaluation index value in the comparative experiment of different sentiment classification models, which verifies the advantages and effectiveness of the model and effectively realizes sentiment analysis on unbalanced review texts.