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AuthorAlqahtani, Abdullah
AuthorUllah Khan, Habib
AuthorAlsubai, Shtwai
AuthorSha, Mohemmed
AuthorAlmadhor, Ahmad
AuthorIqbal, Tayyab
AuthorAbbas, Sidra
Available date2022-12-29T07:18:04Z
Publication Date2022-09-15
Publication NameFrontiers in Computational Neuroscience
Identifierhttp://dx.doi.org/10.3389/fncom.2022.992296
CitationAlqahtani, A., Khan, H. U., Alsubai, S., Sha, M., Almadhor, A., Iqbal, T., & Abbas, S. (2022). An efficient approach for textual data classification using deep learning. Frontiers in Computational Neuroscience, 16.
ISSN1662-5188
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139120374&origin=inward
URIhttp://hdl.handle.net/10576/37781
AbstractText 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.
SponsorQatar University [IRCC-2021-010].
Languageen
PublisherFrontiers
Subjectdeep learning
machine learning
text categorization
text classification
text data
TitleAn efficient approach for textual data classification using deep learning
TypeArticle
Volume Number16
ESSN1662-5188
dc.accessType Open Access


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