Text Classification Using Long Short-Term Memory With GloVe Features

Abstract

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations concerning large- scale dataset training. In this case, a multi-label text classification technique is needed to be able to group four labels from the news article dataset. Deep Learning is a proposed method for solving problems in text classification techniques. This experiment was conducted using one of the methods of Deep Learning Recurrent Neural Network with the application of the architecture of Long Short-Term Memory (LSTM). In this study, the model is based on trial and error experiments using LSTM and 300-dimensional word embedding features with Global Vector (GloVe). By tuning the parameters and comparing the eight proposed LSTM models with a large- scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95. Besides, LSTM with the GloVe feature gets graphic results that are close to good-fit on average.