Text Emotion Analysis Based on TF-IDF and Multihead Attention Transformer Model

Text emotion analysis is an important task in natural language processing. Aiming at the problem that the existing calculation methods can not fully deal with the text datasets with high complexity and confusion, a text emotion analysis model based on TF-IDF (Term Frequency-Inverse Document Frequency) and multihead attention Transformer model is proposed. In the text pre-processing stage, TF-IDF algorithm is used to preliminarily screen words that have a greater impact on the text's emotional orientation, leaving out common stop words and proper nouns that have less impact on the text's emotional orientation from the neighborhood of other texts. After that, the multihead attention Transformer model encoder is used for feature extraction to capture the important semantic information inside the text, which improves the model's semantic analysis and generalization ability. The experimental results show that this model achieves 98.17% accuracy in the multi-field and multi-type comment corpus dataset, which is significantly improved compared with other groups of comparative models.