LSRD-Net: A fine-grained sentiment analysis method based on log-normalized semantic relative distance

Abstract

With the development of AI technology and increasing scene demands, research on fine-grained sentiment analysis gradually replaces sentence-level or document-level coarse-grained sentiment analysis. However, most of the existing fine-grained sentiment analysis (i.e., aspect-based sentiment analysis) relies heavily on the traditional attention mechanism and does not incorporate prior knowledge for assisted recognition in aspect sentiment focusing, ignoring the importance of aligning aspect terms with sentiment information. Therefore, considering the linguistic conventions when expressing emotions, we propose a Log-SRD-based neural network model named LSRD-Net, aiming to improve the recognition accuracy and alignment efficiency of aspect terms and sentiment tendencies. The model uses the logarithmic function to normalize the semantic relative distance (SRD) matrix, then introduces the optimized matrix into the operation of the attention mechanism to achieve the introduction of a prior knowledge, and improves the alignment of aspect term and sentiment information by means of the improved cross-attention mechanism. To validate the effectiveness of the LSRD-Net, several comparative and ablation experiments are conducted on four fine-grained sentiment analysis datasets. The analysis and evaluation of experimental results demonstrate that the LSRD-Net achieves the state-of-the-art performance.