Text Classification Using Long Short-Term Memory With GloVe Features
Winda Kurnia Sari, Dian Palupi Rini, Reza Firsandaya Malik
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 with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (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