Sequential Models for Text Classification Using Recurrent Neural Network
Winda Kurnia Sari, Dian Palupi Rini, Reza Firsandaya Malik, Iman Saladin B. Azhar · 2020
Neural network-based applications are recently shown promising results for text classification.However, it is still challenging for the model to contemplate local features and word contingent on the information of the sentence.This work proposed a deep learning approach to generate a more precise sentence that leverages the preceding texts when classifying a subsequent one.One of the deep learning methods used is Recurrent Neural Network (RNN) with the architecture Long Short-Term Memory (LSTM).By training four variant models of 1-layer LSTM for each balance dataset in pre-processing process with 20,000, 25,000, 30,000, 35,000, 40,000, and 45,000 using optimizer Adam and RMSProp.The results show that; first, the more data input, the higher the accuracy it gets and the second is Adam can perform better as optimizer than RMSProp in this research.The highest Precision, Recall, and F1-score obtain are 97.