The emergence of social media has enabled the swift proliferation of explanatory content across various domains, encompassing advertisements, movies, politics, and economics. The growth has been ascertained through the escalation in emotional analysis, encompassing diverse facets. The Arabic review and OMCD big data sets were amalgamated with Twitter data. The textual content was segregated from emojis, and various word embedding techniques were applied, including Spacy (W2V), Fast Text, and Arabic Bidirectional Encoding Representation (AraBERT), resulting in record-high percentages. Convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were employed in the development of sentiment analysis models. The assessment of model performance was predicated on achieving optimal accuracy, utilizing the voting method to formulate decisions based on weighting. Deep learning (DL) methodologies with the AST (Arabic et al.) dataset resulted in accurate textual content and emoji models. The symbols occupied exact proportions and significantly impacted the final classification. In contrast, for OMCD (Moroccan Offensive Comments Dataset), the text had a more significant impact due to its small number of symbols.
REFERENCES
1.
Brahimi
, B.
, Touahria
, M.
, & Tari
, A.
(2021
). Improving sentiment analysis in Arabic: A combined approach
. Journal of King Saud University-Computer and Information Sciences
, 33
(10
), p. 1242
–1250
.2.
Alwan
, J. K.
, Hussain
, A. J.
, Abd
, D. H.
, Sadiq
, A. T.
, Khalaf
, M.
, & Liatsis
, P.
(2021
). Political Arabic articles orientation using rough set theory with sentiment lexicon
. IEEE Access
, 9
, 24475
–24484
.3.
A. Khalid
Al-mashhadany
, A. T.
Sadiq
,S. Mazin
Ali
, & Abbas
Ahmed
, Healthcare assessment for beauty centers using hybrid sentiment analysis, indones
. J. Electr. Eng. Comput. Sci.
, 28
(2
), p. 890
, 2022
.4.
Ali A.
Tuama
, Ahmed T.
Sadiq
, Twitter sentiment analysis and events prediction using lexicon sentiment analysis for iraqi vernacular
|, solid state technology
, 63
(6
) 2020
5.
Aydo
˘gan
, E., Akcayol
, M.A.
: A comprehensive survey for sentiment analysis tasks using machine learning techniques. In: international symposium on innovations in intelligent
. IEEE
2016
, 1
–7
(2016
)6.
Omar
, A.
, Mahmoud
, T. M.
, & Abd-El-Hafeez
, T.
(2020
). Comparative performance of machine learning and deep learning algorithms for Arabic hate speech detection in osns
. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020
) (pp. 247
–257
). Springer International Publishing
.7.
Yoo
, B.
, & Rayz
, J. T.
(2021
, April). Understanding emojis for sentiment analysis
. In The International FLAIRS Conference Proceedings
(Vol. 34
).8.
Shiha
, M.
, & Ayvaz
, S.
(2017
). The effects of emoji in sentiment analysis
. Int. J. Comput. Electr. Eng.(IJCEE.
), 9
(1
), 360
–369
.9.
Lou
, Y.
, Zhang
, Y.
, Li
, F.
, Qian
, T.
, & Ji
, D.
(2020
). Emojibased sentiment analysis using attention networks
. ACM Transactions on asian and low-resource language information processing (TALLIP)
, 19
(5
), 1
–13
.10.
Althobaiti
, M. J.
(2022
). BERT-based approach to arabic hate speech and offensive language detection in Twitter: exploiting emojis and sentiment analysis
. International Journal of Advanced Computer Science and Applications
, 13
(5
).11.
Essefar
, K.
, Ait
Baha
, H., El Mahdaouy
, A.
, El Mekki
, A.
, & Berrada
, I.
(2023
). OMCD: Offensive Moroccan comments dataset
. Language Resources and Evaluation
, 57
(4
), 1745
–1765
.12.
Babu
, N. V.
, & Kanaga
, E. G. M.
(2022
). Sentiment analysis in social media data for depression detection using artificial intelligence: a review
. In sn computer science
(vol. 3
, issue 1
). Springer. 13.
Dey
, P.
, & Dey
, S.
(2023
). Sentiment analysis of text and emoji data for twitter network
. Al-bahir journal for engineering and pure sciences
, 3
(1
). 14.
Li
, I.
, & Wang
, X. T.
(2023
). Nonverbal communication with emojis in social media: dissociating hedonic intensity from frequency
. Language resources and evaluation
, 57
(1
), 323
–342
. 15.
Berggren
, L.
(2023
). Sentiment Analysis for Swedish: The Impact of Emojis on Sentiment Analysis of Swedish Informal Texts
.16.
Cui
, H.
, Lin
, Y.
, & Utsuro
, T.
(2018
). Sentiment analysis of tweets by CNN utilizing tweets with emoji as training data
. In Proc. Workshop Issues Sentiment Discovery Opinion Mining (WISDOM)
(pp. 1
–8
).17.
Nirbhik
, N.
, & Kumar
, S.
(2023
, October). Integrating Text and Emoticons for Detecting Extremist Affiliations on Twitter using Deep Learning
. In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA
) (pp. 1
–6
). IEEE
.18.
Wang
, P.
, Zhou
, F.
, Guan
, K
& Tan
, W.
(2020
). In vivo therapeutic response monitoring by a self-reporting upconverting covalent organic framework nanoplatform
. Chemical science
, 11
(5
), 1299
–1306
19.
Althobaiti
, M. J.
(2022
). BERT-based approach to arabic hate speech and offensive language detection in Twitter: exploiting emojis and sentiment analysis
. International Journal of Advanced Computer Science and Applications
, 13
(5
)20.
Abdellaoui
, H.
, & Zrigui
, M.
(2018
). Using tweets and emojis to build tead: an Arabic dataset for sentiment analysis
. Computación y Sistemas
, 22
(3
), 777
–786
.21.
Ayvaz
, S.
, & Shiha
, M. O.
(2017
). The effects of emoji in sentiment analysis
. International journal of computer and electrical engineering
, 9
(1
), 360
22.
Li
, I.
, & Wang
, X. T.
(2023
). Nonverbal communication with emojis in social media: dissociating hedonic intensity from frequency
. Language resources and evaluation
, 57
(1
), 323
–342
. 23.
Yinxia
, I. O. U.
, Zhang & donghong
, J. I.
(2020
). Emoji-based sentiment analysis using attention networks
. Acm transactions on asian and low-resource language information processing
, 19
(5
). 24.
Hakami
, S. A. A.
, Hendley
, R. J
& Smith
, P.
(2022
, December). Emoji sentiment roles for sentiment analysis: A case study in arabic texts
. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP
) (pp. 346
–355
)25.
Nassif
, A. B.
, Elnagar
, A.
, Shahin
, I.
, & Henno
, S.
(2021
). Deep learning for arabic subjective sentiment analysis: challenges and research opportunities
. In applied soft computing
(vol. 98
). Elsevier ltd. 26.
Bojanowski
, P.
, Grave
, E.
, Joulin
, A.
& Mikolov
, T.
(2016
). Enriching word vectors with subword information
. Arxiv preprint arxiv:1607.04606.27.
Mikolov
, T.
, Chen
, K.
, Corrado
, G.
& Dean
, J.
(2013a
). Efficient estimation of word representations in vector space
. Arxiv preprint arxiv:1301.378128.
Chantrapornchai
, C.
, & Tunsakul
, A.
(2021
). Information extraction tasks based on BERT and SpaCy on tourism domain
. ECTI Transactions on Computer and Information Technology (ECTI-CIT)
, 15
(1
), 108
–122
.29.
A.
Joulin
, E.
Grave
, P.
Bojanowski
& T.
Mikolov
. Bag of tricks for efficient text classification
,| in proceedings of the 15th conference of the european, short papers
, 2017
.30.
Zahraa
Hasan
. (2023
). Deep Learning for Super Resolution and Applications
. Galoitica Journal of Mathematical Structures and Applications
, 8
(2
), 34
–42
(Doi : )31.
Barbara
Charchekhandra
. (2023
). Align and fusion two thermal and visual images
. Pure Mathematics For Theoretical Computer Science
, 1
(1
), 17
–31
(Doi : )32.
A. H.
Ombabi
, W.
Ouarda
, & A. M.
Alimi
, Deep learning cnn– lstm framework for arabic sentiment analysis using textual information shared in social networks
,| soc.
, vol. 10
, no. 1
, 2020
33.
Antoun
, Wissam
, Fady
Baly
& Hazem
Hajj
. "Arabert: transformer-based model for arabic language understanding
." arxiv preprint arxiv:2003.00104 (2020
).34.
Bensoltane
, Rajae
, & Taher
Zaki
. "Towards arabic aspect-based sentiment analysis: a transfer learning-based approach
." social network analysis and mining
12
(2022
): 1
–16
.
This content is only available via PDF.
© 2025 Author(s). Published under an exclusive license by AIP Publishing.
2025
Author(s)
You do not currently have access to this content.
