Traditional models suffer from feature sparsity,feature loss and incomplete comment feature extraction problems due to the imbalance of comment length.This paper proposes an emotional analysis approach based on dynamic word-sentence features and self-attention(DWSF-SA),to alleviate the incomplete extraction problem caused by the imbalance of text size under batch training.DWSF-SA first follows pre-training on dynamic feature embedding,then employs sentence vectors to complete the less parts and represents the truncated parts by fixed length.Moreover,DWSF-SA also introduces a self-attention mechanism to dynamically integrate the word-sentence fusion features,and makes optimization on the weight parameters to accelerate the computation and training.The ablation and comparison experiments on publicly available datasets demonstrate that the proposed DWSF-SA outperforms traditional approaches in accuracy metrics.
维普
万方数据