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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" }