Sentiment prediction based on neural network

Abstract

Recent years have seen a surge in interest in deep learning, and numerous new neural network models have appeared. In this study, we use CNN and LSTM, two popular deep learning models, to predict sentiment. We use the bidirectional model for the LSTM model. We make predictions by integrating the feature vectors of both sides after receiving sentences inputted in the reverse order from both ends. The recurrent neural network layer is replaced with the CNN model, which defines numerous one-dimensional convolution kernels to conduct convolution operations on the input. The two models are assessed and compared based on how well they anticipate and analyze sentence emotion tones. By comparison, we discover that the BiLSTM model's prediction accuracy on the test set is 10% greater than CNN's, despite requiring more training time.