Deep analysis of an Arabic sentiment classification system based on lexical resource expansion and custom approaches building

Touahri, Ibtissam; Mazroui, Azzeddine

doi:10.1007/s10772-020-09758-z

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…A resource type is a category of resource that is defined by the value of one or more attributes. On the one hand, the attributes and main characteristics of artificial intelligence teaching resources should be considered when classifying them [4].…”

Section: Construction Of Aerobics Course Online Teachingmentioning

confidence: 99%

Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

Zhao

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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In light of the current uneven distribution of aerobics course online resources, this paper presents research on the classification method of aerobics course online teaching resources based on artificial intelligence technology, constructs the display education resource management system based on artificial intelligence technology, realizes the classification of teaching information characteristics, and constructs the classification and evaluation algorithm of aerobiology course online resources. The online teaching resource classification of aerobics course achieves its design goal. Finally, the experiment shows that the artificial intelligence-based online teaching resource classification method for aerobics course is highly practicable and fully meets the research requirements.

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Section: Construction Of Aerobics Course Online Teachingmentioning

confidence: 99%

Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

Zhao

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…The list containing top K keywords is then expanded to include similar semantic words from the Almaany Arabic-to-Arabic dictionary. Almaany is an online dictionary that provides corresponding meanings with similar semantic words for each term in Arabic and has been widely used by in Arabic researches (Touahri and Mazroui, 2021;Al-Matham and Al-Khalifa, 2021). We retrieve all the synonyms provided by Almaany for each crisis keyword if the corresponding meaning of the top keyword is related to the crisis type.…”

Section: # #"mentioning

confidence: 99%

Domain Adaptation for Arabic Crisis Response

Alrashdi¹,

O’Keefe²

2022

Proceedings of the the Seventh Arabic Natural Language Processing Workshop (WANLP)

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Deep learning algorithms can identify related tweets to reduce the information overload that prevents humanitarian organisations from using valuable Twitter posts. However, they rely heavily on human-labelled data, which are unavailable for emerging crises. Because each crisis has its own features, such as location, time and social media response, current models are known to suffer from generalising to unseen disaster events when pre-trained on past ones.Tweet classifiers for low-resource languages like Arabic has the additional issue of limited labelled data duplicates caused by the absence of good language resources. Thus, we propose a novel domain adaptation approach that does not rely on human-labelled data to automatically label tweets from emerging Arabic crisis events to be used to train a model along with available human-labelled data. We evaluate our work on data from seven 2018-2020 Arabic events from different crisis types (flood, explosion, virus and storm). Results show that our method outperforms self-training in classifying crisis-related tweets in real-time scenarios.

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“…S. Hossam et al [135] used Arabic tweets and reviews data and got the highest accuracy of 95%. Aloqaily et al [137], Nahar et al [122] , Alhammi et al [138], Touahri et al [139] and Abdullah et al [90] also used lexicon based techniques on various data sets for Arabic text classification. We conclude that lexicon techniques can help get insights into data.…”

Section: Lexicon Based Text Classificationmentioning

confidence: 99%

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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Deep analysis of an Arabic sentiment classification system based on lexical resource expansion and custom approaches building

Cited by 8 publications

References 27 publications

Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

Domain Adaptation for Arabic Crisis Response

Classification of Arabic Tweets: A Review

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