Aspect-based sentiment analysis (ABSA) task aim at associating a piece of
text with a set of aspects and meanwhile infer their respective sentimental
polarities. The state-of-the-art approaches are built upon fine-tuning of
various pre-trained language models. They commonly attempt to learn
aspect-specific representation from the corpus. Unfortunately, the aspect is
often expressed implicitly through a set of representatives and thus renders
implicit mapping process unattainable unless sufficient labeled examples are
available. However, high-quality labeled examples may not be readily available
in real-world scenarios. In this paper, we propose to jointly address aspect
categorization and aspect-based sentiment subtasks in a unified framework.
Specifically, we first introduce a simple but effective mechanism to construct
an auxiliary-sentence for the implicit aspect based on the semantic information
in the corpus. Then, we encourage BERT to learn the aspect-specific
representation in response to the automatically constructed auxiliary-sentence
instead of the aspect itself. Finally, we empirically evaluate the performance
of the proposed solution by a comparative study on real benchmark datasets for
both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it
consistently achieves state-of-the-art performance in terms of aspect
categorization and aspect-based sentiment across all datasets and the
improvement margins are considerable. The code of BERT-ASC is available in
GitHub: this https URL.

本文提出了一种基于辅助句子和 BERT-ASC 的方法，来解决在缺乏标注数据的情况下，针对文本中暗含的方面进行情感分析和分类的问题，并在真实数据集上进行了有效性验证和性能评估。