Comparison of Pre-Trained Word Vectors for Arabic Text Classification Using Deep Learning Approach

Alwehaibi, Ali

doi:10.1109/icmla.2018.00239

Cited by 39 publications

(25 citation statements)

References 12 publications

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“…In [55] paper, MLP did not revert to high accuracy in two studies, mostly around 50%. The CNN result was better than the MLP once but lower than DBN and LSTM in the following papers [47][48][49], respectively. DBN reverted to high results, but it was only tested for one study [56].…”

Section: Quality Assessmentmentioning

confidence: 90%

“…Both were used in four studies. LSTM were used in [47][48][49][50]. Convolutional neural networks (CNN) were used to classify Arabic text in [50][51][52][53].…”

Section: Quality Assessmentmentioning

confidence: 99%

“…Accuracy was used in 8 studies in our systematic review as shown in Table 4, but not all of these studies identified it well. Accuracy was identified well and the researchers cleared the definition or the equation in only four studies: [48][49][50]57].…”

Section: Quality Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

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Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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Section: Quality Assessmentmentioning

confidence: 90%

“…Both were used in four studies. LSTM were used in [47][48][49][50]. Convolutional neural networks (CNN) were used to classify Arabic text in [50][51][52][53].…”

Section: Quality Assessmentmentioning

confidence: 99%

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A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

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show abstract

“…The effect of utilising different pre-trained word embeddings was explored with the LSTM network in Alwehaibi and Roy [66]. Arabic-news [67], AraFT [68] and AraVec [69] were the three Arabic pre-trained word embeddings that are used in the experiment.…”

Section: Sentiment Analysismentioning

confidence: 99%

Deep learning in Arabic sentiment analysis: An overview

Alharbi

Taileb

Kalkatawi

2019

Journal of Information Science

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Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.

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“…The spelling direction of the Arabic language is 28 letters from right to left. Most Arabic words are morphologically reproduced the literary root list, which is a triple, quadruple, or quintet literary [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks

Gwad¹,

Ismael²,

Gültepe³

2020

IJEAT

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The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.

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Comparison of Pre-Trained Word Vectors for Arabic Text Classification Using Deep Learning Approach

Cited by 39 publications

References 12 publications

A systematic review of text classification research based on deep learning models in Arabic language

A systematic review of text classification research based on deep learning models in Arabic language

Deep learning in Arabic sentiment analysis: An overview

Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks

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