An improved long short-term memory with denoising autoencoder for solving text classification problems

Abstract

In the past decade, text classification has been a popular research field for automatically categorizing documents into relevant categories. Predicting classes for input samples based on their features is the goal of classification. To limit the dimension of the feature space for precise text categorization, feature selection approaches are widely used for recognizing important and affective features while ignoring unimportant, weak, and chaotic components. This paper proposes a new model referred to as DAE-LSTM for reducing data dimension through the use of a denoising autoencoder. (Long Short-Term Memory (LSTM) is one of the popular text classification approaches, particularly when dealing with sequential datasets. Additionally, the Rectified Linear Unit (Relu) activation function was used instead of the hyperbolic tangent activation function (tanh) to improve the accuracy of the model. Four benchmark datasets were used to evaluate the proposed model against 4 cutting-edge text classification methods, including conventional Bidirectional LSTM, Bidirectional GRU, CNN-LSTM, and CNN-GRU. The suggested model has been found to perform much better than current methods using several kinds of performance assessment measures.