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

Mostefa Kara; Abdelkader Laouid; Ahcene Bounceur; Omar Aldabbas

doi:10.1145/3584202.3584216

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

Mostefa Kara
, Abdelkader Laouid
, Ahcene Bounceur
, Omar Aldabbas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a 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 in the accuracy of the system, where we have achieved a better accuracy of 85.31.

Original language	English
Title of host publication	Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022
Publisher	Association for Computing Machinery
Pages	87-92
Number of pages	6
ISBN (Electronic)	9781450399050
DOIs	https://doi.org/10.1145/3584202.3584216
State	Published - 15 Dec 2022
Externally published	Yes
Event	6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022 - Tashkent, Uzbekistan Duration: 15 Dec 2022 → 16 Dec 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022
Country/Territory	Uzbekistan
City	Tashkent
Period	15/12/22 → 16/12/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

Algerian dialect
Emotional detection
Opinion mining
Sentiment analysis
Social web.

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3584202.3584216

Cite this

Kara, M., Laouid, A., Bounceur, A., & Aldabbas, O. (2022). Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study. In Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022 (pp. 87-92). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3584202.3584216

Kara, Mostefa ; Laouid, Abdelkader ; Bounceur, Ahcene et al. / Arabic Opinion Mining Using Machine Learning Techniques : Algerian Dialect as a Case of Study. Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022. Association for Computing Machinery, 2022. pp. 87-92 (ACM International Conference Proceeding Series).

@inproceedings{412a3d6efc9441e6bbd500d610221601,

title = "Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study",

abstract = "Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a 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 in the accuracy of the system, where we have achieved a better accuracy of 85.31.",

keywords = "Algerian dialect, Emotional detection, Opinion mining, Sentiment analysis, Social web.",

author = "Mostefa Kara and Abdelkader Laouid and Ahcene Bounceur and Omar Aldabbas",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022 ; Conference date: 15-12-2022 Through 16-12-2022",

year = "2022",

month = dec,

day = "15",

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language = "English",

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Kara, M, Laouid, A, Bounceur, A & Aldabbas, O 2022, Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study. in Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 87-92, 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022, Tashkent, Uzbekistan, 15/12/22. https://doi.org/10.1145/3584202.3584216

Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study. / Kara, Mostefa; Laouid, Abdelkader; Bounceur, Ahcene et al.
Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022. Association for Computing Machinery, 2022. p. 87-92 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Arabic Opinion Mining Using Machine Learning Techniques

T2 - 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022

AU - Kara, Mostefa

AU - Laouid, Abdelkader

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AU - Aldabbas, Omar

PY - 2022/12/15

Y1 - 2022/12/15

N2 - Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a 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 in the accuracy of the system, where we have achieved a better accuracy of 85.31.

AB - Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to a 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 in the accuracy of the system, where we have achieved a better accuracy of 85.31.

KW - Algerian dialect

KW - Emotional detection

KW - Opinion mining

KW - Sentiment analysis

KW - Social web.

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M3 - Conference contribution

AN - SCOPUS:85159781189

T3 - ACM International Conference Proceeding Series

SP - 87

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BT - Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022

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Kara M, Laouid A, Bounceur A, Aldabbas O. Arabic Opinion Mining Using Machine Learning Techniques: Algerian Dialect as a Case of Study. In Proceedings of the 2022 6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022. Association for Computing Machinery. 2022. p. 87-92. (ACM International Conference Proceeding Series). doi: 10.1145/3584202.3584216

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this