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- W2964853677 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." @default.
- W2964853677 created "2019-08-13" @default.
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- W2964853677 date "2019-07-01" @default.
- W2964853677 modified "2023-09-25" @default.
- W2964853677 title "Linguistically independent sentiment analysis using convolutional-recurrent neural networks model" @default.
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- W2964853677 doi "https://doi.org/10.1109/tsp.2019.8768887" @default.
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