Machine learning-based model for sentiment and sarcasm detection

Aya H. Allam; Hanya M. Abdallah; Eslam Amer; Hamada A. Nayel

Machine learning-based model for sentiment and sarcasm detection

Aya H. Allam
, Hanya M. Abdallah
, Eslam Amer
, Hamada A. Nayel

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

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Abstract

Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.

Original language	English
Title of host publication	WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop
Editors	Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
Publisher	Association for Computational Linguistics
Pages	386-389
Number of pages	4
ISBN (Electronic)	9781954085091
Publication status	Published - 19 Apr 2021
Event	6th Arabic Natural Language Processing Workshop, WANLP 2021 - Virtual, Kyiv, Ukraine Duration: 19 Apr 2021 → …

Conference

Conference	6th Arabic Natural Language Processing Workshop, WANLP 2021
Country/Territory	Ukraine
City	Virtual, Kyiv
Period	19/04/21 → …

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2021.wanlp-1.51Final published version, 186 KBLicence: CC BY

https://aclanthology.org/2021.wanlp-1.51/

Cite this

Allam, A. H., Abdallah, H. M., Amer, E., & Nayel, H. A. (2021). Machine learning-based model for sentiment and sarcasm detection. In N. Habash, H. Bouamor, H. Hajj, W. Magdy, W. Zaghouani, F. Bougares, N. Tomeh, I. A. Farha, & S. Touileb (Eds.), WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop (pp. 386-389). Association for Computational Linguistics. https://aclanthology.org/2021.wanlp-1.51/

Allam, Aya H. ; Abdallah, Hanya M. ; Amer, Eslam et al. / Machine learning-based model for sentiment and sarcasm detection. WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop. editor / Nizar Habash ; Houda Bouamor ; Hazem Hajj ; Walid Magdy ; Wajdi Zaghouani ; Fethi Bougares ; Nadi Tomeh ; Ibrahim Abu Farha ; Samia Touileb. Association for Computational Linguistics, 2021. pp. 386-389

@inproceedings{c8c948e50b5d4eefb2e1c1cd5fe973e7,

title = "Machine learning-based model for sentiment and sarcasm detection",

abstract = "Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.",

author = "Allam, \{Aya H.\} and Abdallah, \{Hanya M.\} and Eslam Amer and Nayel, \{Hamada A.\}",

note = "Publisher Copyright: {\textcopyright} WANLP 2021 - 6th Arabic Natural Language Processing Workshop. Noissn; 6th Arabic Natural Language Processing Workshop, WANLP 2021 ; Conference date: 19-04-2021",

year = "2021",

month = apr,

day = "19",

language = "English",

pages = "386--389",

editor = "Nizar Habash and Houda Bouamor and Hazem Hajj and Walid Magdy and Wajdi Zaghouani and Fethi Bougares and Nadi Tomeh and Farha, \{Ibrahim Abu\} and Samia Touileb",

booktitle = "WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop",

publisher = "Association for Computational Linguistics",

}

Allam, AH, Abdallah, HM, Amer, E & Nayel, HA 2021, Machine learning-based model for sentiment and sarcasm detection. in N Habash, H Bouamor, H Hajj, W Magdy, W Zaghouani, F Bougares, N Tomeh, IA Farha & S Touileb (eds), WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop. Association for Computational Linguistics, pp. 386-389, 6th Arabic Natural Language Processing Workshop, WANLP 2021, Virtual, Kyiv, Ukraine, 19/04/21. <https://aclanthology.org/2021.wanlp-1.51/>

Machine learning-based model for sentiment and sarcasm detection. / Allam, Aya H.; Abdallah, Hanya M.; Amer, Eslam et al.
WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop. ed. / Nizar Habash; Houda Bouamor; Hazem Hajj; Walid Magdy; Wajdi Zaghouani; Fethi Bougares; Nadi Tomeh; Ibrahim Abu Farha; Samia Touileb. Association for Computational Linguistics, 2021. p. 386-389.

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

TY - GEN

T1 - Machine learning-based model for sentiment and sarcasm detection

AU - Allam, Aya H.

AU - Abdallah, Hanya M.

AU - Amer, Eslam

AU - Nayel, Hamada A.

N1 - Publisher Copyright: © WANLP 2021 - 6th Arabic Natural Language Processing Workshop. Noissn

PY - 2021/4/19

Y1 - 2021/4/19

N2 - Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.

AB - Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.

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UR - https://aclanthology.org/2021.wanlp-1.51/

M3 - Conference contribution

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EP - 389

BT - WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop

A2 - Habash, Nizar

A2 - Bouamor, Houda

A2 - Hajj, Hazem

A2 - Magdy, Walid

A2 - Zaghouani, Wajdi

A2 - Bougares, Fethi

A2 - Tomeh, Nadi

A2 - Farha, Ibrahim Abu

A2 - Touileb, Samia

PB - Association for Computational Linguistics

T2 - 6th Arabic Natural Language Processing Workshop, WANLP 2021

Y2 - 19 April 2021

ER -

Machine learning-based model for sentiment and sarcasm detection

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