Publication result detail

Language-Independent Text Classifier Base on Recurrent Neural Networks

MYŠKA, V.

Original Title

Language-Independent Text Classifier Base on Recurrent Neural Networks

English Title

Language-Independent Text Classifier Base on Recurrent Neural Networks

Type

Paper in proceedings (conference paper)

Original Abstract

This paper deals with a proposal of language independent text classifiers based on recurrent neural networks. They work at a character level thus they do not require any text preprocessing. The classifiers have been trained and evaluated on a multilingual data set that is privately collected from film review databases. It contains Czech (Slovak), English, German and Spanish language subset. The resulting accuracy of the proposed language independent classifiers base on the recurrent neural networks in polarity sentiment analysis task is 78.55%.

English abstract

This paper deals with a proposal of language independent text classifiers based on recurrent neural networks. They work at a character level thus they do not require any text preprocessing. The classifiers have been trained and evaluated on a multilingual data set that is privately collected from film review databases. It contains Czech (Slovak), English, German and Spanish language subset. The resulting accuracy of the proposed language independent classifiers base on the recurrent neural networks in polarity sentiment analysis task is 78.55%.

Keywords

sentiment analysis;recurrent neural networks;deep learning

Key words in English

sentiment analysis;recurrent neural networks;deep learning

Authors

MYŠKA, V.

RIV year

2020

Released

25.04.2019

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5735-5

Book

Proceedings of the 25th Conference STUDENT EEICT 2019

Pages from

754

Pages to

758

Pages count

5

BibTex

@inproceedings{BUT157417,
  author="Vojtěch {Myška}",
  title="Language-Independent Text Classifier Base on Recurrent Neural Networks",
  booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
  year="2019",
  pages="754--758",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5735-5"
}

Documents

512-763-1-SM