Due to the rapid increase in User-Generated Content (UGC) data, opinion mining, also called sentiment analysis, has attracted much attention in both academia and industry. Aspect-Based Sentiment Analysis (ABSA), a subfield of sentiment analysis, aims to extract the aspect and the corresponding sentiment simultaneously. Previous works in ABSA may generate undesired aspects, require a large amount of training data, or produce unsatisfactory results. This paper proposes a Graph Neural Network based method to automatically generate aspect-specific sentiment words using a small number of aspect seed words and general sentiment words. It subsequently leverages the aspect-specific sentiment words to improve the Joint Aspect-Sentiment Autoencoder (JASA) model. We conduct experiments on two datasets to verify the proposed model. It shows that our approach has better performance in the ABSA task when compared with previous works.
CITATION STYLE
Tsai, Y. H., Chang, C. M., Chen, K. H., & Hwang, S. Y. (2022). An Integration of TextGCN and Autoencoder into Aspect-Based Sentiment Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13428 LNCS, pp. 3–16). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-12670-3_1
Mendeley helps you to discover research relevant for your work.