Attribute-level Sentiment Analysis Incorporating Auxiliary Information

Huiyang Yin; Jin Hou

doi:10.54097/4qvjf884

Authors

Huiyang Yin
Jin Hou

DOI:

https://doi.org/10.54097/4qvjf884

Keywords:

Emotional classification, Aspect-level emotional analysis, Attention mechanism.

Abstract

In order to obtain the sentiment tendency of multiple attributes contained in the data reviews, this paper uses an attribute-level sentiment analysis model RBT-BiAtt-GCN, which incorporates auxiliary information and attention mechanism, to analyze the attribute-level sentiment analysis of e-commerce reviews of citrus. The model incorporates auxiliary information, attention mechanism, and GCN network on the basis of the previous one, and the final macro F1 value obtained can reach 78.21%, which achieves good results in comparison with other models.

Downloads

Download data is not yet available.

References

FU X, LIU G, GUO Y, et al. Multi-aspect blog sentiment analysis based on LDA topic model and hownet lexicon; proceedings of the International Conference on Web Information Systems and Mining, F, 2011 [C]. Springer.

AKHTAR N, ZUBAIR N, KUMAR A, et al. Aspect based sentiment oriented summarization of hotel reviews [J]. Procedia computer science, 2017, 115: 563-71.

LUO H, LI T, LIU B, et al. Improving aspect term extraction with bidirectional dependency tree representation [J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 27(7): 1201-12.

RUBTSOVA Y, KOSHELNIKOV S. Aspect extraction from reviews using conditional random fields; proceedings of the International Conference on Knowledge Engineering and the Semantic Web, F, 2015 [C]. Springer.

XU H, LIU B, SHU L, et al. Double embeddings and cnn-based sequence labeling for aspect extraction [J]. ar Xiv preprint arXiv:180504601, 2018.

CHEN P, SUN Z, BING L, et al. Recurrent attention network on memory for aspect sentiment analysis; proceedings of the Proceedings of the 2017 conference on empirical methods in natural language processing, F, 2017 [C].

PHAN H T, NGUYEN N T, HWANG D. Convolutional attention neural network over graph structures for improving the performance of aspect-level sentiment analysis [J]. Information Sciences, 2022.

LI R, CHEN H, FENG F, et al. Dual graph convolutional networks for aspect-based sentiment analysis; proceedings of the Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), F, 2021 [C].

XIAO Z, WU J, CHEN Q, et al. BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based