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Papers/Parameterized Convolutional Neural Networks for Aspect Lev...

Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

Binxuan Huang, Kathleen M. Carley

2019-09-13EMNLP 2018 10Sentiment AnalysisSentiment ClassificationGeneral ClassificationClassification
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Abstract

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.

Results

TaskDatasetMetricValueModel
Sentiment AnalysisSemEval-2014 Task-4Laptop (Acc)70.06PF-CNN
Sentiment AnalysisSemEval-2014 Task-4Mean Acc (Restaurant + Laptop)74.63PF-CNN
Sentiment AnalysisSemEval-2014 Task-4Restaurant (Acc)79.2PF-CNN
Aspect-Based Sentiment Analysis (ABSA)SemEval-2014 Task-4Laptop (Acc)70.06PF-CNN
Aspect-Based Sentiment Analysis (ABSA)SemEval-2014 Task-4Mean Acc (Restaurant + Laptop)74.63PF-CNN
Aspect-Based Sentiment Analysis (ABSA)SemEval-2014 Task-4Restaurant (Acc)79.2PF-CNN

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