Manara - Qatar Research Repository
Browse
10.3389_fncom.2022.992296.pdf (929.91 kB)

An efficient approach for textual data classification using deep learning

Download (929.91 kB)
journal contribution
submitted on 2023-09-24, 06:47 and posted on 2023-09-25, 12:01 authored by Abdullah Alqahtani, Habib Ullah Khan, Shtwai Alsubai, Mohemmed Sha, Ahmad Almadhor, Tayyab Iqbal, Sidra Abbas

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.

Other Information

Published in: Frontiers in Computational Neuroscience
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3389/fncom.2022.992296

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Frontiers

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Business and Economics - QU

Usage metrics

    Qatar University

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC