Author:
Alqahtani Abdullah,Ullah Khan Habib,Alsubai Shtwai,Sha Mohemmed,Almadhor Ahmad,Iqbal Tayyab,Abbas Sidra
Abstract
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.
Subject
Cellular and Molecular Neuroscience,Neuroscience (miscellaneous)
Cited by
4 articles.
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