Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
This work addresses sentiment analysis for specific aspects in text, which is an incremental improvement over existing methods.
The paper tackled aspect-level sentiment classification by introducing a parameterized convolutional neural network that incorporates aspect information through parameterized filters and gates, achieving excellent results on SemEval 2014 datasets.
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.