Affiliation:
1. School of Education, City University of Macau, Macau 999078, China
2. College of Chinese Language and Culture, Jinan University, Guangzhou 510632, China
Abstract
The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective.
Reference31 articles.
1. Feng, C., Li, H., Zhao, H., Xue, Y., and Tang, J. (November, January 30). Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks. Proceedings of the 19th Chinese National Conference on Computational Linguistics, Hainan, China.
2. A Review on Opinion Mining and Sentiment Analysis;Shaikh;Int. J. Comput. Appl.,2016
3. Aspect-Level Sentiment Classification for Sentences Based on Dependency Tree and Distance Attention;Su;Comput. Res. Dev.,2019
4. Tang, D., Qin, B., and Liu, T. (2016). Aspect level sentiment classification with deep memory network. arXiv.
5. Wang, Y., Huang, M., Zhu, X., and Zhao, L. (2016, January 1–5). Attention-based LSTM for aspect-level sentiment classification. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.