Modeling Category Semantic and Sentiment Knowledge for Aspect-Level Sentiment Analysis

To classify the sentiment polarity of the aspect entity in a sentence, most existing research evaluates the semantic knowledge among a certain aspect of a sentence and corresponding context as significant clues for the task. However, available accompanying information has not been completely exploited, especially the coarse-grained category-level knowledge in contexts. Such knowledge can help to alleviate polysemy and ambivalence problems. In this paper, we propose a multi-task learning framework Co-interactive Attention Network(CoAN) to jointly learn and handle multiple granularity features at both target and category levels. In order to leverage the fine-grained and coarse-grained knowledge in contexts and get multi-granularity sentiment related sentence representations, we introduce two co-interactive attention layers to conduct accompanying semantic interactions at the word-level and the feature-level. The experimental results on three restaurant review datasets prove that CoAN is superior to the baselines by 1.41% in accuracy and 2.81% in F1-score. Furthermore, ablation studies and attention visualizations show that the multi-task framework and novel co-interactive mechanisms can distinguish and fuse multi-granularity knowledge, which benefits the two subtasks in aspect based sentiment analysis.