Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities. Some joint models have been proposed to address this task. Given a text, these joint models detect all the aspect categories mentioned in the text and predict the sentiment polarities toward them at once. Although these joint models obtain promising performances, they train separate parameters for each aspect category and therefore suffer from data deficiency of some aspect categories. To solve this problem, we propose a novel joint model which contains a shared sentiment prediction layer. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval-2016 Datasets demonstrate the effectiveness of our model.
翻译:Aspectle类情感分析(ACSA)旨在预测文本中提及的方方面面类别及其相应的情绪极化。一些联合模型是针对这项任务提出的。根据一个文本,这些联合模型检测了文本中提及的方方面面类别,并同时预测了对方情绪极化。虽然这些联合模型取得了有希望的性能,但它们为每个方方面类提供了不同的参数,因此也存在某些方方面面的数据缺陷。为了解决这个问题,我们提出了一个包含共同情绪预测层的新颖的联合模型。共享情绪预测层在各方方面面类别之间传递了情感知识,缓解了数据缺陷造成的问题。在SemEval-2016数据集上进行的实验显示了我们模型的有效性。