Graph convolutional neural networks for sentiment analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU136947" target="_blank" >RIV/00216305:26220/20:PU136947 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Graph convolutional neural networks for sentiment analysis

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings I of the 26th Conference STUDENT EEICT 2020

ISBN

978-80-214-5867-3

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

340-344

Publisher name

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Place of publication

Brno

Event location

BRNO

Event date

Apr 23, 2020

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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