Detail publikace

Linguistically independent sentiment analysis using convolutional-recurrent neural networks model

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

Originální název

Linguistically independent sentiment analysis using convolutional-recurrent neural networks model

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

deep learning; machine learning; sentiment analysis; text classification

Autoři

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

Vydáno

4. 7. 2019

Nakladatel

IEEE

Místo

Budapest, Hungary

ISBN

978-1-7281-1864-2

Kniha

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

Strany od

212

Strany do

215

Strany počet

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"
}