Emotion Analysis Based on Neural Network under the Big Data Environment

Abstract

Aiming at the problems of poor emotional tendency prediction effect and low utilization of syntactic information, this study proposes a big data sentiment analysis method based on neural network. First, the BERT model is used to vectorize the input data to reduce the semantic loss when the data is vectorized; then the word vector is input into the bidirectional LSTM encoder to obtain data features. Finally, the representation of the attention layer is used as the final feature vector for sentiment classification, reducing the influence of irrelevant data. The experimental results show that the method has high accuracy, recall, and F1 value and can effectively improve the accuracy of fine-grained sentiment classification of ambiguous texts.