RWTLA-Prompt: Leveraging Prompt Learning and Deep Networks for Sentiment Analysis

With the rapid development of social media, sentiment analysis has become a vital tool for understanding online public opinion and user attitudes. To enhance the model’s capacity for capturing nuanced emotional semantics, this paper proposes a novel sentiment analysis approach that integrates soft prompt learning with a hybrid neural network architecture. Specifically, we leverage RoBERTa-WWM to obtain rich semantic representations, and combine TextCNN and BiLSTM in a parallel structure to extract both local and contextual features. An attention mechanism is further incorporated to enhance the model’s focus on emotionally salient words.To improve task adaptability, we design soft prompt templates by extracting key information from input sentences using an extractive summarization method. These soft prompts are then concatenated with the original input and fed into the model for training and classification. Without significantly increasing the number of trainable parameters, our approach retains the pre-trained model’s language understanding capabilities while enhancing sentiment prediction performance.Experiments conducted on three representative datasets demonstrate that our proposed model outperforms both traditional hard prompt methods and baseline models without prompt learning. The optimized soft prompt templates achieve superior accuracy and F1 scores, validating the effectiveness and generalizability of our approach.