An efficient approach for textual data classification using deep learning
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
FrontiersPublication 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