Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
About
We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.
Binxuan Huang, Kathleen M. Carley• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Aspect Sentiment Classification | Rest SemEval 2014 (test) | Accuracy78.43 | 73 | |
| Aspect Sentiment Classification | Laptop (test) | Accuracy86.35 | 49 | |
| Aspect-based Sentiment Classification | 15Rest SemEval-2015 (test) | Accuracy0.7501 | 32 | |
| Aspect-based Sentiment Classification | Rest SemEval 2016 (test) | Accuracy81.34 | 28 | |
| Aspect-based Sentiment Analysis | Lap SemEval 2014 (test) | Accuracy69.72 | 20 | |
| Aspect-level 3-way Sentiment Classification | Restaurant (test) | Accuracy0.792 | 7 | |
| Aspect-level Binary Sentiment Classification | Restaurant (test) | Accuracy90.58 | 7 |
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