Aspect-Level Sentiment Classification Based on Auto-Adaptive Model Transfer

Abstract

Aspect-level sentiment classification (ASC) is a fine-grained sentiment analysis task that involves detecting the sentiment polarity of a specific opinion target in a given sentence. Despite the popularity of deep learning methods, the limited availability of ASC training data has resulted in the suboptimal performance of neural network models. To address this issue, researchers have proposed transferring resource-rich document-level sentiment knowledge to ASC tasks using model transfer and shared parameters. However, these methods have neglected the impact of differences in the training data for different tasks on the shared layer parameters. Therefore, this paper proposes a transfer method that automatically adapts to task and domain differences - Auto-adaptive Model Transfer (AAMT). This method considers adding specific disturbance variables on shared layer parameters to subdivide task differences to further obtain more accurate and effective transfer knowledge. In addition, on this basis, we introduce an attention mechanism to combine aspect and sentence features to better capture the sentiment vocabulary associated with a given target. A series of experiments on three public datasets show that our proposed method can solve the problem of insufficient training data for ASC and achieve good results.