Sequence Prediction Model for Aspect-Level Sentiment Classification
Qianlong Wang, Jiangtao Ren · Frontiers in artificial intelligence and applications · 2020
Aspect-level sentiment classification aims to distinguish the sentiment polarity of each aspect in a given sentence. It is more complex than text-level sentiment classification in that it is a fine-grained task. Existing methods, which formulate this task as predicting the sentiment polarity of a provided (sentence, aspect) pair, tend to ignore the relationship between the sentiment polarity of aspects. In this paper, we propose a sequence prediction model with a sentiment polarity fusion module which sequentially predicts the sentiment polarity of each aspect within sentence. Besides, we use the temporal attention mechanism to keep track of what has been focused on, which discourages repeated attention to the context words with strong sentiment polarity when predicting the sentiment polarity of different aspects. Experimental results on five benchmarking collections illustrate that our proposed model3 outperforms a range of baseline models by a substantial margin, and further demonstrate that the relationship between the sentiment polarity of aspects is helpful to solve the aspect-level sentiment classification.