NNDF: A New Neural Detection Network for Aspect-Category Sentiment Analysis

Lijian Li, Yuanpeng He, Li Li

Abstract: Aspect-category sentiment analysis (ACSA) is crucial for capturing and understanding sentiment polarities of aspect categories hidden behind in sentences or documents automatically. Nevertheless, existing methods have not modeled semantic dependencies of aspect terms and specified entity’s aspect category in sentences. In this paper, we propose a New Neural Detection Network, named NNDF in short, to enhance the ACSA performance. Specifically, representations of input sentences and aspect categories contained in our method are generated by a CNN-pooling-BiLSTM structure respectively, where sentences are represented based on their contextual words and aspect categories are represented based on word embeddings of entities category-specific. Then, a Transformer-based encoder is used to model implicit dependency of sentence contexts and aspect categories of entities in sentences. Finally, the embedding of aspect-category is learned by the novel bidirectional attention mechanism for the sentiment classification. Besides, experiments conducted on Restaurant and MAMS benchmark datasets for the task demonstrate that NNDF achieves more accurate prediction results as compared to several state-of-the-art baselines.