Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study

Mostefa Kara; Abdelkader Laouid; Ahcène Bounceur; Omar Aldabbas

doi:10.20944/preprints202301.0443.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 23 January 2023 / Approved: 25 January 2023 / Online: 25 January 2023 (04:11:49 CET)

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to the large number of texts and their diversity in all areas of life. In this manuscript, we apply four algorithms to classify tweets written in the Algerian dialect. To extract feelings, we used six features based on three polarities. In the presented work, we manually annotate a corpus of 2,891 texts and create an Algerian lexicon of idioms that contains 1328 annotated words. Our results show that there are improvements gained on the accuracy of the system, where we have achieved a better accuracy of 85.31%.

Algerian dialect ; Opinion mining ; Sentiment analysis ; Emotional detection ; Social web

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study

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