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Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Authors
  • Albalawi, Yahya1, 2, 3
  • Buckley, Jim1, 3
  • Nikolov, Nikola S.1, 3
  • 1 University of Limerick, Limerick, Ireland , Limerick (Ireland)
  • 2 University of Taibah, Al-Ula, Saudi Arabia , Al-Ula (Saudi Arabia)
  • 3 Lero, University of Limerick, Limerick, Ireland , Limerick (Ireland)
Type
Published Article
Journal
Journal of Big Data
Publisher
Springer Nature
Publication Date
Jul 02, 2021
Volume
8
Issue
1
Identifiers
DOI: 10.1186/s40537-021-00488-w
Source
Springer Nature
Keywords
Disciplines
  • Research
License
Green

Abstract

This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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