Text Classification Using Long Short-Term Memory
Winda Kurnia Sari, Dian Palupi Rini, Reza Firsandaya Malik · 2019
Text classification usually has the basic problem of presenting data with very high dimension, so that the model formed is usually hampered because (1) training time increases exponentially appropriate with the number of feature used and (2) Model has an increased risk of overfitting with a growing number of features. Recurrent Neural Network (RNN) is one of the most popular architectures used in natural language processing (NLP) since the recurrent structure is very suitable for long variable text processing. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. Long Short-Term Memory (LSTM) offers element which is expected to be able to record a feature of input, such as in natural language processing for English, an element record gender of the subject, other element records whether single or plural subject. These features will be found by LSTM itself in the training process. With variant word sequence features, the results of this study have the highest accuracy of 82.13%.