Modelling Context and Syntactical Features for Aspect ...

Modelling Context and Syntactical Features for Aspect -based Sentiment Analysis Minh Hieu Phan & Philip Ogunbona School of Computer Science and Software Engineering University of Wollongong, NSW 2522, Australia Abstract focused on one of the two sub-tasks alone. Rep- resentative works include (Xu et al., 2018; Da'u The Aspect -based sentiment analysis (ABSA) and Salim, 2019; Poria et al., 2016) for Aspect ex- consists of two conceptual tasks, namely an traction and (Zeng et al., 2019; Huang et al., 2018;. Aspect extraction and an Aspect sentiment clas- sification. Rather than considering the tasks Song et al., 2019; Thet et al., 2010) for Aspect sen- separately, we build an end-to-end ABSA so- timent classification. Recent approaches (He et al., lution. Previous works in ABSA tasks did 2019; Wang et al., 2018; Li et al., 2019) attempted not fully leverage the importance of syntac- to develop an integrated solution to solve both tasks tical information. Hence, the Aspect extrac- simultaneously by formulating both sub-tasks as tion model often failed to detect the bound- a single sequence labelling with a unified tagging aries of multi-word Aspect terms.

Song et al.,2019;Thet et al.,2010) for aspect sen-timent classification. Recent approaches (He et al., 2019;Wang et al.,2018;Li et al.,2019) attempted to develop an integrated solution to solve both tasks simultaneously by formulating both sub-tasks as a single sequence labelling with a unified tagging scheme. Adding unified tokens ...

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