by Jakub Nowak, Ahmet Taspinar, Rafa\l Scherer
Abstract:
Recurrent neural networks are increasingly used to classify text data, displacing feed-forward networks. This article is a demonstration of how to classify text using Long Term Term Memory (LSTM) network and their modifications, i.e. Bidirectional LSTM network and Gated Recurrent Unit. We present the superiority of this method over other algorithms for text classification on the example of three sets: Spambase Data Set, Farm Advertisement and Amazon book reviews. The results of the first two datasets were compared with AdaBoost ensemble of feedforward neural networks. In the case of the last database, the result is compared to the bag-of-words algorithm. In this article, we focus on classifying two groups in the first two collections, since we are only interested in whether something is classified into a SPAM or an eligible message. In the last dataset, we distinguish three classes.
Reference:
LSTM Recurrent Neural Networks for Short Text and Sentiment Classification (Jakub Nowak, Ahmet Taspinar, Rafa\l Scherer), Chapter in (Leszek Rutkowski, Marcin Korytkowski, Rafa\l Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada, eds.), Springer International Publishing, 2017.
Bibtex Entry:
@Inbook{Nowak2017,
author="Nowak, Jakub
and Taspinar, Ahmet
and Scherer, Rafa{\l}",
editor="Rutkowski, Leszek
and Korytkowski, Marcin
and Scherer, Rafa{\l}
and Tadeusiewicz, Ryszard
and Zadeh, Lotfi A.
and Zurada, Jacek M.",
title="LSTM Recurrent Neural Networks for Short Text and Sentiment Classification",
bookTitle="Artificial Intelligence and Soft Computing: 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part II",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="553--562",
abstract="Recurrent neural networks are increasingly used to classify text data, displacing feed-forward networks. This article is a demonstration of how to classify text using Long Term Term Memory (LSTM) network and their modifications, i.e. Bidirectional LSTM network and Gated Recurrent Unit. We present the superiority of this method over other algorithms for text classification on the example of three sets: Spambase Data Set, Farm Advertisement and Amazon book reviews. The results of the first two datasets were compared with AdaBoost ensemble of feedforward neural networks. In the case of the last database, the result is compared to the bag-of-words algorithm. In this article, we focus on classifying two groups in the first two collections, since we are only interested in whether something is classified into a SPAM or an eligible message. In the last dataset, we distinguish three classes.",
isbn="978-3-319-59060-8",
doi="10.1007/978-3-319-59060-8_50",
url="https://doi.org/10.1007/978-3-319-59060-8_50"
}