Scalable multi-label Arabic text classification

Ahmed, Nizar A.; Shehab, Mohammed A.; Al‐Ayyoub, Mahmoud; Hmeidi, Ismail

doi:10.1109/iacs.2015.7103229

Cited by 30 publications

(15 citation statements)

References 24 publications

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“…The developed model was trained using the standard corpus which is collected in [33], it contains 10,000 Arabic articles written in "modern standard Arabic language (MSA)", where those articles are assigned to five labels: Sports, Economy, Arts, Science, and Politics. In addition, three multi-label evaluation metrics were used to evaluate the given Arabic datasets which are average recall, average precision, and average Fmeasure.…”

Section: B Pt Methods Based On Binary Classificationmentioning

confidence: 99%

“…The study conducted in [33] aimed to handle the MLC problem of Arabic language based on transformation to multiclass classification approach using a set of single-label machine learning classifiers. The authors aimed to transform the MLC problem of Arabic data into several single-label classification problems by using MEKA tool to perform PT methods which are: LP, BR, and Ranking and Threshold-based method (RT).…”

Section: Pt Methods Based On Multi-class Classificationmentioning

confidence: 99%

“…Relevant studies conducted on Arabic text using machine learning algorithms [33], [5], [34], [4]. [35].…”

Section: Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Label Arabic Text Classification: An Overview

Aljedani¹,

Alotaibi²,

Taileb³

2020

IJACSA

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There is a massive growth of text documents on the web. This led to the increasing need for methods that can organize and classify electronic documents (instances) automatically. Multi-label classification task is widely used in real-world problems and it has been applied on different applications. It assigns multiple labels for each document simultaneously. Few and insufficient research studies have investigated the multi-label text classification problem in the Arabic language. Therefore, this survey paper aims to present an extensive review of the existing multi-label classification methods and techniques that can deal with multi-label problem. Besides, we focus on Arabic language by covering the relevant applications of multi-label classification on the Arabic text, and identify the main challenges faced by these studies. Furthermore, this survey presents an experimental comparisons of different multi-label classification methods applied for the Arabic context and points out some baseline results. We found that further investigations are also needed to improve the multi-label classification task in the Arabic language, especially the hierarchical classification task.

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Section: B Pt Methods Based On Binary Classificationmentioning

confidence: 99%

Section: Pt Methods Based On Multi-class Classificationmentioning

confidence: 99%

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Multi-Label Arabic Text Classification: An Overview

Aljedani¹,

Alotaibi²,

Taileb³

2020

IJACSA

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“…Kusumaningrum, et al [25], similar in principle to this paper, considered classification of Indonesian news articles using latent Dirichlet allocation. Also [26] dwelt on Arabic text classification. The motivation to explore MNB for the current problem comes from the findings presented by [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring multinomial naïve Bayes for Yorùbá text document classification

Ayogu¹

2020

Nig. J. Tech.

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The recent increase in the emergence of Nigerian language text online motivates this paper in which the problem of classifying text documents written in Yorùbá language into one of a few pre-designated classes is considered. Text document classification/categorization research is well established for English language and many other languages; this is not so for Nigerian languages. This paper evaluated the performance of a multinomial Naive Bayes model learned on a research dataset consisting of 100 samples of text each from business, sporting, entertainment, technology and political domains, separately on unigram, bigram and trigram features obtained using the bag of words representation approach. Results show that the performance of the model over unigram and bigram features is comparable but significantly better than a model learned on trigram features. The results generally indicate a possibility for the practical application of NB algorithm to the classification of text documents written in Yorùbá language. Keywords: Supervised learning, text classification, Yorùbá language, text mining, BoW Representation

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“…Arabic is the language of 22 countries with more than 400 million inhabitants. Natural Language Processing for the Arabic language is considered challenging due to the special characteristics of the language such as: orthography, the existence of short vowels, the complex morphology compared to English, the widespread of synonyms and the lack of publicly and freely accessible corpora [29], [35], [10].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews

Al‐Ayyoub¹,

Nuseir²,

Kanaan³

et al. 2016

ijacsa

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Abstract-Sentiment Analysis (SA) is one of hottest fields in data mining (DM) and natural language processing (NLP). The goal of SA is to extract the sentiment conveyed in a certain text based on its content. While most current works focus on the simple problem of determining whether the sentiment is positive or negative, Multi-Way Sentiment Analysis (MWSA) focuses on sentiments conveyed through a rating or scoring system (e.g., a 5-star scoring system). In such scoring systems, the sentiments conveyed in two reviews of close scores (such as 4 stars and 5 stars) can be very similar creating an added challenge compared to traditional SA. One intuitive way of handling this challenge is via a divide-and-conquer approach where the MWSA problem is divided into a set of sub-problems allowing the use of customized classifiers to differentiate between reviews of close scores. A hierarchical classification structure can be used with this approach where each node represents a different classification sub-problem and the decision from it may lead to the invocation of another classifier. In this work, we show how the use of this divide-and-conquer hierarchical structure of classifiers can generate better results than the use of existing flat classifiers for the MWSA problem. We focus on the Arabic language for many reasons such as the importance of this language and the scarcity of prior works and available tools for it. To the best of our knowledge, very few papers have been published on MWSA of Arabic reviews. One notable work is that of Ali and Atiya, in which the authors collected a large scale Arabic Book Reviews (LABR) dataset and made it publicly available. Unfortunately, the baseline experiments on this dataset had very low accuracy. We present two different hierarchical structures and compare their accuracies with the flat structure using different core classifiers. The comparison is based on standard accuracy measures such as precision and recall in addition to using the mean squared error (MSE) as a more accurate measure given the fact that not all misclassifications are the same. The results show that, in general, hierarchical classifiers give significant improvements (of more than 50% in certain cases) over flat classifiers.

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Scalable multi-label Arabic text classification

Cited by 30 publications

References 24 publications

Multi-Label Arabic Text Classification: An Overview

Multi-Label Arabic Text Classification: An Overview

Exploring multinomial naïve Bayes for Yorùbá text document classification

Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews

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