A Short Text Affective Analysis Model Combining Adversary Training and ERNIE
Text sentiment classification using deep learning techniques is a hot research topic in the field of natural language processing in recent years,and good text representation is a key factor in improving the classification performance of deep learning models.A text sentiment analysis model PERNIE_RCNN that includes adversarial training is proposed,as short texts contain little sentiment information and are susceptible to noise interference during training.The model uses the ERNIE pre-trained model to vectorize the input text and initially extract the sentiment features of the text.The model then adds noise perturbations to the output vector of the ERNIE pre-training model to generate adversarial samples against the original samples,and feeds the generated adversarial samples into the classification model for adversarial training to improve the robustness of the model against noise attacks.The experimental results show that the PERNIE_RCNN model has better text classification performance and better generalisation ability.
short text sentiment analysisdeep learningadversarial trainingtext classification
NETL
NSTL
万方数据
