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LSTM Recurrent Neural Networks for Short Text and Sentiment Classification

Nowak, Jakub ; Taspinar, Ahmet ; Scherer, Rafał

Artificial Intelligence and Soft Computing, p.553-562 [Periódico revisado por pares]

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Título:
LSTM Recurrent Neural Networks for Short Text and Sentiment Classification
Autor: Nowak, Jakub ; Taspinar, Ahmet ; Scherer, Rafał
Assuntos: Bidirectional LSTM ; Gated Recurrent Unit ; Long Short-Term Memory ; Recurrent neural networks ; Sentiment classification ; Text classification
É parte de: Artificial Intelligence and Soft Computing, p.553-562
Descrição: 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.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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