Arabic Topic Detection Using Discriminative Multi nominal Naïve Bayes and Frequency Transforms

Alsanad, Ahmed

doi:10.1145/3297067.3297095

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…This classifier is widely used in text classification and medical diagnoses. Alsanad et al [76], Alsaleem et al [79], Al-Osaimi et al [81] used NB classifier along with other classifiers for Arabic text classification on various data sets. From these Al-Osaimi et al [81], gained a valuable accuracy from other technique.…”

Section: Discussion and Learned Lessonsmentioning

confidence: 99%

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Classification of Arabic Tweets: A Review

Alruily

2021

Electronics

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Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

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Section: Discussion and Learned Lessonsmentioning

confidence: 99%

“…Alsanad et al [76] presented an article in which they used corpus based method for the classification of Arabic tweets. They classified tweets into three distinct categories.…”

Section: Supervised Leaning Techniquesmentioning

confidence: 99%

Classification of Arabic Tweets: A Review

Alruily

2021

Electronics

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Recent developments in information extraction approaches from Arabic tweets on social networking sites

Alzahrani¹,

Zaidi²

2022

Int. j. adv. appl. sci.

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Information extraction from Arabic tweets has attracted the attention of researchers due to the huge data accessibility for the swift expansion of social media platforms. With the increasing use of social web applications, information extraction from the various platforms has gained importance for understanding the trending post and events predictions based on those sentiments written by the users on certain news feeds. The Arabic Language is mostly used in Middle Eastern and African countries and most users tweet on social media using the Arabic language, therefore Arabic text classification and sentiment analysis aimed to predict information extraction from social media platforms. This research provides a more detailed critical review of the information extraction presented in the literature focused on using different tools, methods, and techniques like k-NN, support vector machines, Naïve Bayes, and other machine learning tools for the data extraction and processing.

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Arabic Topic Detection Using Discriminative Multi nominal Naïve Bayes and Frequency Transforms

Cited by 2 publications

References 13 publications

Classification of Arabic Tweets: A Review

Classification of Arabic Tweets: A Review

Recent developments in information extraction approaches from Arabic tweets on social networking sites

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