Sentiment Analysis of Micro-blog on Public Health Emergency with Prompt Embedding

[Objective] At the early stage of public health emergencies, limited Weibo posts and informal expressions lead to ineffective sentiment analysis. We propose a sentiment analysis model for Weibo posts based on prompt embedding and emotion feature fusion to address this issue. [Methods] First, we extracted the sentiment information from Weibo posts based on the emotional dictionary. Then, we used the pre-trained RoBERTa model to establish semantic and sentiment vectors. We also embedded prompts as prefixes for the semantic vectors. Third, we utilized the Transformer encoder and attention mechanism to extract semantic and emotional features. We also computed the sample feature weights using the focal loss function. Finally, we combined the semantic and emotional features to conduct sentiment analysis. [Results] We examined the new model with Weibo comments on the outbreak of COVID-19 in Shenzhen. The accuracy and F1 score of the model reached 93.46% and 93.49%, which were 6.78% and 6.97% higher than the baseline BERT model. [Limitations] Weibo data contains a large amount of images and videos. However, our model did not include multi-modal fusion for sentiment analysis. [Conclusions] The proposed model could improve the effectiveness of sentiment classification with a small sample data size. © 2023 Chinese Academy of Sciences. All rights reserved.