Abstract
Aspect-Category based Sentiment Analysis (ACSA) aims to predict the aspect category and the sentiment polarity mentioned in a sentence. Most works treat it as two individual tasks: aspect category detection (ACD) and aspect category sentiment classification (ACSC), thus resulting in category missing and mismatch between sentiment words and aspect categories. This paper proposes a dual-attention based joint aspect sentiment classification model (AS-DATJM), which jointly predicts aspect category and sentiment polarity in one framework. Given a sentence, AS-DATJM firstly employs aspect aware attention in ACD to obtain the hidden aspect terms. With these terms as guidance, ACSC module aggregates relevant sentiment context over the Graph Convolutional Network. As a result, the inter-relations between aspect categories and sentiments can be captured and employed to predict both categories simultaneously. Extensive evaluations demonstrate the effctiveness of our model and results show that it outperforms the state-of-the-art methods on four benchmark datasets.
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Gu, P., Zhang, Z. (2022). Dual-Attention Based Joint Aspect Sentiment Classification Model. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_17
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