Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment Classification - Li, Xianyong; Ding, Li; Du, Yajun; Fan, Yongquan; Shen, Fashan (Frontiers in Psychology, 2022)
Aspect-level sentiment classification is an interesting and challenging research task to identify the sentiment polarities of aspect words in sentences. Previous attention-based methods rarely consider the position information of aspect and contextual words. For an aspect word in a sentence, its adjacent words should be given more attention than the long distant words. Based on this consideration, this paper designs a position influence vector to represent the position information between an aspect word and the context. By combining the position influence vector, multi-head self-attention mechanism and bidirectional gated recurrent unit (BiGRU), a position-enhanced multi-head self-attention network based BiGRU (PMHSAT-BiGRU) model is proposed. To verify the effectiveness of the proposed model, this paper makes a large number of experiments on SemEval2014 restaurant, SemEval2014 laptop, SemEval2015 restaurant and SemEval2016 restaurant data sets. The experiment results show that the performance of the proposed PMHSAT-BiGRU model is obviously better than the baselines. Specially, compared with the original LSTM model, the Accuracies of the proposed PMHSAT-BiGRU model on the four data sets are improved by 5.72%, 6.06%, 4.52% and 3.15%, respectively.