Research on Text Sentiment Classification and Recognition Based on Improved BiLSTM and TextCNN
Qiupeng Li · 2024
Due to the rapid development of Internet technology, social media and online communication have become essential platforms for people to express their emotions. Text sentiment classification and recognition have also reached a high-quality development stage. In response to this demand, this paper proposes a text sentiment classification and recognition method based on improved BiLSTM and TextCNN. First, we build data acquisition and preprocessing modules to obtain and process a large amount of text data. Second, the recognition modules are designed, including BiLSTM and TextCNN model structure, and the classification accuracy is improved through model training and parameter optimization. The experimental results show that the proposed method has high accuracy and stability in sentiment classification tasks. By analyzing the experimental results, conclusions are drawn about the effectiveness and feasibility of improving the BiLSTM and TextCNN models.