A Multi-Layer Network for Aspect-Based Cross-Lingual Sentiment Classification
| Autor: | Kalim Sattar, Qasim Umer, Dinara G. Vasbieva, Sungwook Chung, Zohaib Latif, Choonhwa Lee |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Access, Vol 9, Pp 133961-133973 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2169-3536 28494490 |
| DOI: | 10.1109/ACCESS.2021.3116053 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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