Publication detail

Graph convolutional neural networks for sentiment analysis

MYŠKA, V.

Original Title

Graph convolutional neural networks for sentiment analysis

Type

conference paper

Language

English

Original 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.

Keywords

sentiment analysis, graph neural networks, deep learning

Authors

MYŠKA, V.

Released

23. 4. 2020

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5867-3

Book

Proceedings I of the 26th Conference STUDENT EEICT 2020

Pages from

340

Pages to

344

Pages count

5

BibTex

@inproceedings{BUT164699,
  author="Vojtěch {Myška}",
  title="Graph convolutional neural networks for sentiment analysis",
  booktitle="Proceedings I of the 26th Conference STUDENT EEICT 2020",
  year="2020",
  pages="340--344",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5867-3"
}