Mining Novel Customer Needs from Online Product Review
Abstract
1 Introduction
2 Relevant Work
3 Method
3.1 BERT as the Feature Extractor
3.2 Model Training
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4 Experiment
4.1 Dataset
4.2 Dataset Experiment Result
Model | Precision (overall) | Recall (overall) | F1 (overall) |
fastText | 0.9394 | 0.5533 | 0.5927 |
CNN | 0.9951 | 0.5667 | 0.6152 |
BiLSTM | 0.8750 | 0.6661 | 0.7293 |
BERT | 0.9060 | 0.8524 | 0.8773 |
Testing class | Precision (overall) | Recall (overall) | F1 (overall) |
Positive class | 0.8154 | 0.7067 | 0.7571 |
Negative class | 0.9967 | 0.9982 | 0.9974 |
Overall macro average | 0.9060 | 0.8524 | 0.8773 |
5 Conclusion
Acknowledgments
References
Index Terms
- Mining Novel Customer Needs from Online Product Review
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Publisher
Association for Computing Machinery
New York, NY, United States
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