Papers with real-world sentiment analysis
Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
| Challenge: | Existing methods for few-shot learning are insufficient to capture task variations in natural language domains. |
| Approach: | They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. |
| Outcome: | The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets. |
Self-training Strategies for Sentiment Analysis: An Empirical Study (2024.findings-eacl)
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| Challenge: | Sentiment analysis is a crucial task in natural language processing. |
| Approach: | They propose to leverage a small amount of labeled and unlabeled data to train models with self-training. |
| Outcome: | The proposed method improves the performance of small language models in several few-shot settings while reducing the cost of annotations. |