Abstract
Aspect-based sentiment analysis (ABSA) is one of the fundamental task in text sentiment analysis which aims to analyze the sentiment polarity of a specific aspect (terms or categories) in a given sentence. Aspect category detection (ACD) and aspect term sentiment analysis (ATSA) are both sub-task of ABSA. However, most of the previous methods regard them as two separate tasks, and ignore the potential relationship between them. In this paper, we propose a multi-task hierarchical graph attention network (MTHGAT) which contains two levels of graph attention networks, a sentence level and a document level. The former is built based on the reshaped dependency parse tree, the latter is built to gather information between sentences in a document. Extensive experiments are conducted on the two restaurant datasets in SemEval-2015 and SemeEval-2016. The results show that our proposed model performs better than most baseline methods.
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Ge, L., Li, J. (2022). MTHGAT: A Neural Multi-task Model for Aspect Category Detection and Aspect Term Sentiment Analysis on Restaurant Reviews. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_23
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