Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models
- Authors
- Pathak, Abhilash; Kumar, Sudhanshu; Roy, Partha Pratim; Kim, Byung-Gyu
- Issue Date
- Nov-2021
- Publisher
- MDPI
- Keywords
- aspect-based sentiment analysis; BERT; classification; ensemble; Hindi
- Citation
- ELECTRONICS, v.10, no.21
- Journal Title
- ELECTRONICS
- Volume
- 10
- Number
- 21
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146139
- DOI
- 10.3390/electronics10212641
- ISSN
- 2079-9292
2079-9292
- Abstract
- Sentiment Analysis is becoming an essential task for academics, as well as for commercial companies. However, most current approaches only identify the overall polarity of a sentence, instead of the polarity of each aspect mentioned in the sentence. Aspect-Based Sentiment Analysis (ABSA) identifies the aspects within the given sentence, and the sentiment that was expressed for each aspect. Recently, the use of pre-trained models such as BERT has achieved state-of-the-art results in the field of natural language processing. In this paper, we propose two ensemble models based on multilingual-BERT, namely, mBERT-E-MV and mBERT-E-AS. Using different methods, we construct an auxiliary sentence from this aspect and convert the ABSA problem to a sentence-pair classification task. We then fine-tune different pre-trained BERT models and ensemble them for a final prediction based on the proposed model; we achieve new, state-of-the-art results for datasets belonging to different domains in the Hindi language.</p>
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