AUTHOR=Alqahtani Abdullah , Ullah Khan Habib , Alsubai Shtwai , Sha Mohemmed , Almadhor Ahmad , Iqbal Tayyab , Abbas Sidra 

TITLE=An efficient approach for textual data classification using deep learning

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=16

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.992296

DOI=10.3389/fncom.2022.992296

ISSN=1662-5188

ABSTRACT=<p>Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.</p>