Balakrishnan, Vimala and Shi, Zhongliang and Law, Chuan Liang and Lim, Regine and Teh, Lee Leng and Fan, Yue (2022) A deep learning approach in predicting products' sentiment ratings: A comparative analysis. Journal of Supercomputing, 78 (5). pp. 7206-7226. ISSN 0920-8542, DOI https://doi.org/10.1007/s11227-021-04169-6.
Full text not available from this repository.Abstract
We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and F-score (91.1%). Individually, RNN was the best model with an accuracy of 87.5% and F-score of 83.5%, while RoBERTa had the best F-score of 73.1%. The study shows that deep learning is better for analyzing the sentiments within the text compared to supervised machine learning and provides a direction for future work and research.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Sentiment rating; Deep learning; Word embeddings; Customer reviews; Ensemble models |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Oct 2023 08:56 |
| Last Modified: | 09 Oct 2023 08:56 |
| URI: | http://eprints.um.edu.my/id/eprint/42414 |
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