Publication detail

Linguistically independent sentiment analysis using convolutional-recurrent neural networks model

MYŠKA, V.BURGET, R.POVODA, L.DUTTA, M.

Original Title

Linguistically independent sentiment analysis using convolutional-recurrent neural networks model

Type

conference paper

Language

English

Original Abstract

Text classification is a process which analyses text and assigns one or more classes to it based on its content. This paper introduces a linguistically independent text classifier based on convolutional–recurrent neural networks. The classifier works at character level instead of some higher structures such as words, sentences, etc. To evaluate the accuracy of the proposed methodology, the Yelp data set and other multilingual data set obtained from film review databases containing Czech, German and Spanish languages were used. The resulting accuracy on the Yelp data set is 93,64 %. We also proved that the proposed model can work for various languages.

Keywords

deep learning; machine learning; sentiment analysis; text classification

Authors

MYŠKA, V.;BURGET, R.;POVODA, L.;DUTTA, M.

Released

4. 7. 2019

Publisher

IEEE

Location

Budapest, Hungary

ISBN

978-1-7281-1864-2

Book

2019 42nd International Conference on Telecommunications and Signal Processing (TSP)

Pages from

212

Pages to

215

Pages count

4

BibTex

@inproceedings{BUT157766,
  author="Vojtěch {Myška} and Radim {Burget} and Lukáš {Povoda} and Malay Kishore {Dutta}",
  title="Linguistically independent sentiment analysis using convolutional-recurrent neural networks model",
  booktitle="2019 42nd International Conference on Telecommunications and Signal Processing (TSP)",
  year="2019",
  pages="212--215",
  publisher="IEEE",
  address="Budapest, Hungary",
  doi="10.1109/TSP.2019.8768887",
  isbn="978-1-7281-1864-2"
}