Chinese Short-Text Sentiment Prediction: A Study of Progressive Prediction Techniques and Attentional Fine-Tuning

Abstract

With sentiment prediction technology, businesses can quickly look at user reviews to find ways to improve their products and services. We present the BertBilstm Multiple Emotion Judgment (BBMEJ) model for small-sample emotion prediction tasks to solve the difficulties of short emotion identification datasets and the high dataset annotation costs encountered by small businesses. The BBMEJ model is suitable for many datasets. When an insufficient quantity of relevant datasets prevents the model from achieving the desired training results, the prediction accuracy of the model can be enhanced by fine-tuning it with additional datasets prior to training. Due to the number of parameters in the Bert model, fine-tuning requires a lot of data, which drives up the cost of fine-tuning. We present the Bert Tail Attention Fine-Tuning (BTAFT) method to make fine-tuning work better. Our experimental findings demonstrate that the BTAFT fine-tuning approach performs better in terms of the prediction effect than fine-tuning all parameters. Our model obtains a small sample prediction accuracy of 0.636, which is better than the ideal baseline of 0.064. The Macro-F1 (F1) evaluation metrics significantly exceed other models.