Unlocking the Power of Long Short-Term Memory Networks: A Text Classification Approach

Text classification typically suffers from the fundamental issue of latter-day data with a very high dimension, which hinders the model developed because training time grows exponentially with the number of features used and model risk of overfitting increases with increasing number of features. Since the recurrent structure is ideal for processing long varied texts, recurrent neural networks (RNNs) are among the most often used designs in natural language processing (NLP). RNN using the Long Short-Term Memory (LSTM) architecture is one of the deep learning techniques suggested in this study. In natural language processing for English, one element stores the subject's gender, while another element records whether the subject is singular or plural. Long Short-Term Memory (LSTM) supplies elements that are intended to be able to record an input characteristic. LSTM will discover these properties throughout the training procedure. The results of this study are most accurate when variable word sequence characteristics are taken into account.