Textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification

Textual emotion recognition is an increasingly popular research area, which recognizes human emotions by capturing textual information posted by people, and the recognition results depend on the composition of the system framework. In this paper, we propose a textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification. Firstly, the text is pre-processed based on ALBERT pre-training model. Then, the word vector-related features are obtained by BiLSTM Recurrent Neural Network for machine learning to make them have a specific form for classification in order to improve the accuracy of emotion recognition. In the link of emotion classification, this paper innovatively proposes a classification method SVM-NB to obtain more emotional polarities. Finally, the classifier is used to obtain the emotional polarities of the text, including positive and negative categories. The negative emotions are divided into three sub-categories of anger, sad and disgust. The experiments show that the proposed emotion recognition method has better robustness and higher accuracy than the general modal recognition method.