A Position-aware Transformation Network for Aspect-level Sentiment Classification
Tao Jiang, Jiahai Wang, Youwei Song, Yanghui Rao · 2019
This paper introduce a novel Position-aware Transformation Network (PTNet) for aspect-level sentiment classification. On the one hand, attention mechanisms have been employed to model the relationship between aspect and context. However, the position information of aspect words is rarely emphasized for sentiment prediction. The truth is that we should pay more attention to the word which is close to the aspect, since the words with closer distance may have a greater impact on the sentiment polarity of a sentence toward the aspect. On the other hand, existing approaches often adopt the average of aspect vectors or context vectors to calculate the attention weights, which may cause information loss if the aspect and context is not a single word. Therefore, this paper propose a position-aware layer and a context transformation layer in our model to solve the above two issues respectively. Moreover, several convolution kernels are also used to extract the n-gram information for prediction. We examine the performance of our model on three datasets: the first two are from SemEval2014 including the reviews of restaurants and laptops, and the third is a tweet collection. Experimental results show that our model consistently outperforms the state-of-the-art methods on all three datasets.