Text Classification Using Neural Networks: A Case Study with Reuters Dataset

Assigning predetermined categories to text documents according to their content is known as text classification, and it is a fundamental task in natural language processing (NLP). With an emphasis on the extensively researched Reuters dataset, this paper provides a thorough examination and application of text classification using neural networks. A strong standard for assessing classification algorithms is provided by the Reuters dataset, which consists of a collection of brief newswires divided into 46 unique and mutually exclusive subjects. To address this challenge, we use a neural network-based architecture constructed with TensorFlow and Keras. The approach entails one-hot encoding of the target labels after preprocessing the textual material using tokenization and vectorization techniques. To improve generalization and avoid overfitting, dropout regularization layers are added to a feedforward neural network in the suggested model architecture. To guarantee effective and precise learning, the training procedure makes use of the Adam optimizer with categorical cross-entropy as the loss function. Our tests demonstrate the neural network's capacity to successfully categorize the Reuters news wires by assessing the model's performance in terms of accuracy and other pertinent metrics. The findings show that a reliable solution for text categorization challenges can be obtained by combining neural networks with sophisticated optimization approaches. In addition to demonstrating deep learning's ability to handle multi-class text categorization, this study acts as a guide for further investigation and applications in related fields.