Evaluation of Impact of Neural Networks in Text Classification

Abstract

One of the most trending and major areas of research in Natural Language Processing (NLP) is the classification of text data. This necessarily means that the category that the text belongs to is determined by the content of the text. Various algorithms such as Recurrent Neural Network along with its variation which is Long Short-Term Memory, Hierarchical Attention Networks and also Convolutional Neural Network have been used to analyse how the context of the text can be determined from the text data which in available in terms of datasets. These algorithms each have a special characteristic of their own. While Recurrent Neural Network maintains the structural sequence of the contexts, the Convolutional Neural Network manages to obtain the n-gram feature and the Hierarchical Attention Network manages the hierarchy of the documents or data. The above said algorithms have been implemented on the British Broadcasting Corporation News datasets. Various parameters such as recall, precision, accuracy etc. have been considered along with standards such as F1-score, confusion matrix etc. to deduce the impact.