A multi-layer network for aspect-based cross-lingual sentiment classificationopen access
- Authors
- Sattar, Kalim; Umer, Qasim; Vasbieva, Dinara G.; Chung, Sungwook; Latif, Zohaib; Lee, Choonhwa
- Issue Date
- Sep-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Sentiment analysis; Task analysis; Data models; Feature extraction; Data mining; Bit error rate; Tagging; Natural language processing; cross-lingual; divided attention; aspect-based sentiment classification
- Citation
- IEEE ACCESS, v.9, pp.133961 - 133973
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 9
- Start Page
- 133961
- End Page
- 133973
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140966
- DOI
- 10.1109/ACCESS.2021.3116053
- ISSN
- 2169-3536
- Abstract
- In the recent era, the advancement of communication technologies provides a valuable interaction source between people of different regions. Nowadays, many organizations adopt the latest approaches, i.e., sentiment analysis and aspect-oriented sentiment classification, to evaluate user reviews to improve the quality of their products. The processing of multi-lingual user reviews is a key challenge in Natural Language Processing (NLP). This paper proposes a multi-layer network with divided attention to perform aspect-based sentiment classification for cross-lingual data. It extracts the Part-of-Speech (POS) tagging information of the given reviews, preprocesses them, and converts them into tokens. Furthermore, bi-lingual dictionaries are leveraged to map the converted tokens from one language to another. Given the preprocessed and mapped reviews, vectors are generated by leveraging the multi-lingual BERT and passed to the proposed deep learning classifier. The 10351 restaurant reviews from SemEval-2016 Task 5 dataset are exploited for the prediction of aspect-based sentiment. The results of cross-lingual validation suggest that the proposed approach significantly outperforms the state-of-the-art approaches and improves the precision, recall, and F1 by more than 23%, 20%, and 22%, respectively.
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