Bengali text classification using ANN

Abstract

Bengali text classification is a challenging task due to the unique characteristics of the Bengali language. In recent years, artificial neural networks have emerged as powerful models for natural language processing tasks, including text classification. This study explores the application of artificial neural networks for Bengali text classification and investigates their performance in accurately categorizing Bengali text documents. The research begins with the collection of a labeled dataset consisting of Bengali text documents from diverse domains. The dataset is preprocessed by tokenizing the text, removing stopwords, and applying stemming to reduce noise and enhance feature representation. Various neural network architectures, such as feedforward neural networks, recurrent neural networks (RNNs), or convolutional neural networks (CNNs), are implemented and trained using the preprocessed dataset. During the training phase, the neural network models learn to extract relevant features and capture the underlying patterns and relationships within the Bengali text. The models are optimized using appropriate loss functions and backpropagation algorithms to minimize classification errors and improve accuracy. Hyperparameter tuning and regularization techniques are employed to prevent overfitting and enhance generalization performance. The trained neural network models are evaluated using standard evaluation metrics, including accuracy. A separate validation or testing dataset is used to assess the models' performance on unseen Bengali text instances. The results of the experiments provide insights into the effectiveness of artificial neural networks for Bengali text classification and comparisons with other traditional machine learning algorithms. Overall, this research contributes to the advancement of Bengali text classification using artificial neural networks, providing valuable insights and guidelines for developing robust and accurate models for automated categorization of Bengali text documents across various domains.ANNs can effectively classify Bengali text documents into predefined categories. The findings highlight the importance of selecting appropriate network architectures, preprocessing techniques to achieve optimal performance in Bengali text classification. The successful implementation of ANN-based Bengali text classification can have significant applications in various domains like topic categorization, document classification, and information retrieval in the Bengali language. It enables the automation of text analysis tasks, thereby facilitating efficient handling and extraction of valuable insights from large volumes of Bengali textual data.