Graph Convolutional Neural Networks For Sentiment Analysis
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Date
2020
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
Commonly used approaches based on deep learning for sentiment analysis task operating over data in Euclidean space. In contrast with them, this paper presents, a novel approach for sentiment analysis task based on a graph convolutional neural networks (GCNs) operating with data in Non-Euclidean space. Text data processed by the approach have to be converted to a graph structure. Our GCNs models have been trained on 25 000 data samples and evaluated 5 000 samples. The Yelp data set has been used. The experiment is focused on polarity sentiment analysis task. Nevertheless, a relatively small training data set has been used, our best model achieved 86.12% accuracy.
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Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers. s. 340-344. ISBN 978-80-214-5867-3
https://conf.feec.vutbr.cz/eeict/EEICT2020
https://conf.feec.vutbr.cz/eeict/EEICT2020
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií