PMID- 36185709
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221004
IS  - 1662-5188 (Print)
IS  - 1662-5188 (Electronic)
IS  - 1662-5188 (Linking)
VI  - 16
DP  - 2022
TI  - An efficient approach for textual data classification using deep learning.
PG  - 992296
LID - 10.3389/fncom.2022.992296 [doi]
LID - 992296
AB  - 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.
CI  - Copyright (c) 2022 Alqahtani, Ullah Khan, Alsubai, Sha, Almadhor, Iqbal and Abbas.
FAU - Alqahtani, Abdullah
AU  - Alqahtani A
AD  - College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz 
      University, Al-Kharj, Saudi Arabia.
FAU - Ullah Khan, Habib
AU  - Ullah Khan H
AD  - Department of Accounting and Information Systems, College of Business and 
      Economics, Qatar University, Doha, Qatar.
FAU - Alsubai, Shtwai
AU  - Alsubai S
AD  - College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz 
      University, Al-Kharj, Saudi Arabia.
FAU - Sha, Mohemmed
AU  - Sha M
AD  - College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz 
      University, Al-Kharj, Saudi Arabia.
FAU - Almadhor, Ahmad
AU  - Almadhor A
AD  - College of Computer and Information Sciences, Jouf University, Al-Kharj, Saudi 
      Arabia.
FAU - Iqbal, Tayyab
AU  - Iqbal T
AD  - Department of Computer Science, FAST-NUCES, Islamabad, Pakistan.
FAU - Abbas, Sidra
AU  - Abbas S
AD  - Department of Computer Science, COMSATS University, Islamabad, Pakistan.
LA  - eng
PT  - Journal Article
DEP - 20220915
PL  - Switzerland
TA  - Front Comput Neurosci
JT  - Frontiers in computational neuroscience
JID - 101477956
PMC - PMC9521674
OTO - NOTNLM
OT  - deep learning
OT  - machine learning
OT  - text categorization
OT  - text classification
OT  - text data
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/10/04 06:00
MHDA- 2022/10/04 06:01
PMCR- 2022/01/01
CRDT- 2022/10/03 04:23
PHST- 2022/07/12 00:00 [received]
PHST- 2022/08/30 00:00 [accepted]
PHST- 2022/10/03 04:23 [entrez]
PHST- 2022/10/04 06:00 [pubmed]
PHST- 2022/10/04 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fncom.2022.992296 [doi]
PST - epublish
SO  - Front Comput Neurosci. 2022 Sep 15;16:992296. doi: 10.3389/fncom.2022.992296. 
      eCollection 2022.