Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification

Binxuan Huang, Kathleen Carley


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.
Anthology ID:
D18-1136
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1091–1096
URL:
https://www.aclweb.org/anthology/D18-1136
DOI:
10.18653/v1/D18-1136
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PDF:
https://www.aclweb.org/anthology/D18-1136.pdf