@inproceedings{301a75a89785430da3451937b3da200a,
title = "Comparison of Text Classification Methods Using Deep Learning Neural Networks",
abstract = "In this article, we tend to examine the text classification task by using various neural networks. A small number of previously classified texts can change the accuracy of the studied text classifiers. This is often vital in many text classification applications because an oversized range of uncategorized data is effortlessly reachable. However, getting an annotated text is a quite challenging task. The article additionally demonstrates that the Convolution Neural Network (CNN) does not demand semantic or syntactic knowledge and can perform in a better way on a words level. Secondly, a Recurrent Neural Network (RNN) model can effectively classify the text data (sequence type). RNN outperforms the other Neural Networks for the sequence test classification task. We used corpora of two different types from separate sources (IMDB and self-created bloggers corpus). The results of our experiments provide evidence that vector representation of the text can improve the score of the task.",
keywords = "Convolution neural networks, Sentiment analysis, Text classification",
author = "Maaz Amjad and Alexander Gelbukh and Ilia Voronkov and Anna Saenko",
note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Switzerland AG.; 20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019 ; Conference date: 07-04-2019 Through 13-04-2019",
year = "2023",
doi = "10.1007/978-3-031-24340-0_33",
language = "Ingl{\'e}s",
isbn = "9783031243394",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "438--450",
editor = "Alexander Gelbukh",
booktitle = "Computational Linguistics and Intelligent Text Processing - 20th International Conference, CICLing 2019, Revised Selected Papers",
address = "Alemania",
}