Papers › Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Binxuan Huang, Kathleen M. Carley
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PF-CNN | Laptop (Acc) | 70.06 | #40 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PF-CNN | Mean Acc (Restaurant + Laptop) | 74.63 | #40 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | PF-CNN | Restaurant (Acc) | 79.20 | #40 of 48 | Archive leaderboard | report |
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