Wednesday, January 27, 2021

Arabic Sentiment Analysis: A Systematic Literature Review


 Abstract

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). 

This paper introduces a systematic review of the existing literature relevant to ASA. 

The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers' search for related studies. 

The findings of the review propose a taxonomy for sentiment classification methods. 

Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. 

Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

REFERENCES

[1] T. T. (et, J.-C. Na, and C. S. Khoo, “Aspect-based sentiment

analysis of movie reviews on discussion boards,” Journal of

Information Science, vol. 36, no. 6, pp. 823–848, 2010.


[2] H. Yu and V. Hatzivassiloglou, “Towards answering opinion

questions: separating facts from opinions and identifying the

polarity of opinion sentences,” in Proceedings of the 2003

Conference on Empirical Methods in Natural Language

Processing, pp. 129–136, Association for Computational

Linguistics, Sapporo, Japan, July 2003.


[3] K. Ravi and V. Ravi, “A survey on opinion mining and

sentiment analysis: tasks, approaches and applications,”

Knowledge-Based Systems, vol. 89, pp. 14–46, 2015.


[4] B. Pang and L. Lee, “Opinion mining and sentiment anal-

ysis,” Foundations and Trends in Information Retrieval,

vol. 2, no. 1–2, pp. 1–135, 2008.


[5] B. Liu and L. Zhang, “A survey of opinion mining and

sentiment analysis,” in Mining Text Data, pp. 415–463,

Springer, Berlin, Germany, 2012.


[6] A. Abbasi, H. Chen, and A. Salem, “Sentiment analysis in

multiple languages: feature selection for opinion classifica-

tion in web forums,” ACM Transactions on Information

Systems (TOIS), vol. 26, no. 3, p. 12, 2008.


[7] A. Farghaly and K. Shaalan, “Arabic natural language processing:

challenges and solutions,” ACM Transactions on Asian Language

Information Processing (TALIP), vol. 8, no. 4, p. 14, 2009.


[8] M. Rushdi-Saleh, M. T. Mart ́ın-Valdivia, L. A. Ureña-L ́opez,

and J. M. Perea-Ortega, “OCA: opinion corpus for Arabic,”

Journal of the American Society for Information Science and

Technology, vol. 62, no. 10, pp. 2045–2054, 2011.


[9] S. S. Alotaibi, “Sentiment analysis in the Arabic language

using machine learning,” Ph. D. dissertation, Colorado State

University, Fort Collins, CO, USA, 2015.


[10] P. D. Turney, “(umbs up or thumbs down?: semantic

orientation applied to unsupervised classification of re-

views,” in Proceedings of the 40th Annual Meeting on As-

sociation for Computational Linguistics, pp. 417–424,

Association for Computational Linguistics, Philadelphia, PA,

USA, July 2002.


[11] R. Tsarfaty, D. Seddah, Y. Goldberg et al., “Statistical parsing

of morphologically rich languages (SPMRL): what, how and

whither,” in Proceedings of the NAACL HLT 2010 First

Workshop on Statistical Parsing of Morphologically-Rich

Languages, pp. 1–12, Association for Computational Lin-

guistics, Los Angeles, CsA, USA, June 2010.


[12] R. T. Khasawneh, H. A. Wahsheh, M. N. Al-Kabi, and

I. M. Alsmadi, “Sentiment analysis of Arabic social media

content: a comparative study,” in 2013 8th International

Conference for Internet Technology and Secured Transactions

(ICITST), pp. 101–106, IEEE, Los Angeles, CA, USA, June

2013.


[13] H. Al-Rubaiee, R. Qiu, and D. Li, “Identifying mubasher

software products through sentiment analysis of Arabic

tweets,” in Proceedings of the 2016 International Conference

on Industrial Informatics and Computer Systems (CIICS),

pp. 1–6, IEEE, Sharjah-Dubai, UAE, March 2016.


[14] M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh,

and B. Gupta, “Deep recurrent neural network vs. support

vector machine for aspect-based sentiment analysis of Arabic

hotels’ reviews,” Journal of Computational Science, vol. 27,

pp. 386–393, 2018.


[15] A.-S. Mohammad, O. Qwasmeh, B. Talafha, M. Al-Ayyoub,

Y. Jararweh, and E. Benkhelifa, “An enhanced framework for

aspect-based sentiment analysis of hotels’ reviews: Arabic

reviews case study,” in Proceedings of the 2016 11th Inter-

national Conference for Internet Technology and Secured

Transactions (ICITST), pp. 98–103, IEEE, Barcelona, Spain,

December 2016.


[16] A. Elouardighi, M. Maghfour, H. Hammia, and F.-z. Aazi, “A

machine learning approach for sentiment analysis in the

standard or dialectal Arabic facebook comments,” in Pro-

ceedings of the 2017 3rd International Conference of Cloud

Computing Technologies and Applications (CloudTech),

pp. 1–8, IEEE, Rabat, Morocco, October 2017.


[17] M. Mataoui, T. E. B. Hacine, I. Tellache, A. Bakhtouchi, and

O. Zelmati, “A new syntax-based aspect detection approach for

sentiment analysis in Arabic reviews,” in Proceedings of the 2018

2nd International Conference on Natural Language and Speech

Processing (ICNLSP), pp. 1–6, IEEE, Trento, Italy, April 2018.


[18] A.-S. Mohammad, M. Al-Ayyoub, H. N. Al-Sarhan, and

Y. Jararweh, “An aspect-based sentiment analysis approach

to evaluating Arabic news affect on readers,” Journal of

Universal Computer Science, vol. 22, no. 5, pp. 630–649, 2016.


[19] Z. Kechaou, A. Wali, M. Ben Ammar, H. Karray, and

A. M. Alimi, “A novel system for video news’ sentiment

analysis,” Journal of Systems and Information Technology,

vol. 15, no. 1, pp. 24–44, 2013.


[20] A. Hammad and A. El-Halees, “An approach for detecting

spam in Arabic opinion reviews,” @e International Arab

Journal of Information Technology, vol. 12, 2013.


[21] C. Okoli and K. Schabram, “A guide to conducting a sys-

tematic literature review of information systems research,”

Sprouts: Working Papers on Information Systems, vol. 10,

no. 26, 2010.


[22] D. Tranfield, D. Denyer, and P. Smart, “Towards a meth-

odology for developing evidence-informed management

knowledge by means of systematic review,” British Journal of

Management, vol. 14, no. 3, pp. 207–222, 2003.


[23] B. Kitchenham and S. Charters, “Guidelines for performing

systematic literature reviews in software engineering,” EBSE

Technical Report (EBSE-2007-01), Keele University, Keele,

UK, 2007.


[24] S. Ahmed, M. Pasquier, and G. Qadah, “Key issues in

conducting sentiment analysis on Arabic social media text,”

in Proceedings of the 2013 9th International Conference on

Innovations in Information Technology (IIT), pp. 72–77,

IEEE, Vancouver, BC, Canada, July 2013.


[25] M. N. Al-Kabi, I. M. Alsmadi, R. T. Khasawneh, and

H. A. Wahsheh, “Evaluating social context in Arabic opinion

mining,” @e International Arab Journal of Information

Technology, vol. 15, no. 6, pp. 974–982, 2018.


[26] R. Duwairi and M. El-Orfali, “A study of the effects of

preprocessing strategies on sentiment analysis for Arabic

text,” Journal of Information Science, vol. 40, no. 4,

pp. 501–513, 2014.


[27] M. N. Al-Kabi, N. A. Abdulla, and M. Al-Ayyoub, “An

analytical study of Arabic sentiments: maktoob case study,”

in Proceedings of the 2013 8th International Conference for

Internet Technology and Secured Transactions (ICITST),

pp. 89–94, IEEE, London, UK, December 2013.


[28] M. M. Ashi, M. A. Siddiqui, and F. Nadeem, “Pre-trained

word embeddings for Arabic aspect-based sentiment analysis

of airline tweets,” in Proceedings of the International Con-

ference on Advanced Intelligent Systems and Informatics,

pp. 241–251, Springer, Cairo, Egypt, September 2018.


[29] A. Elnagar, “Investigation on sentiment analysis for Arabic

reviews,” in Proceedings of the 2016 IEEE/ACS 13th Inter-

national Conference of Computer Systems and Applications

(AICCSA), pp. 1–7, IEEE, Agadir, Morocco, November 2016.


[30] A. Elnagar and O. Einea, “Brad 1.0: book reviews in Arabic

dataset,” in Proceedings of the 2016 IEEE/ACS 13th Inter-

national Conference of Computer Systems and Applications

(AICCSA), pp. 1–8, IEEE, Agadir, Morocco, November 2016.


[31] W. Cherif, A. Madani, and M. Kissi, “Towards an efficient

opinion measurement in Arabic comments,” Procedia

Computer Science, vol. 73, pp. 122–129, 2015.


[32] M. Itani, C. Roast, and S. Al-Khayatt, “Corpora for sentiment

analysis of Arabic text in social media,” in Proceedings of the

2017 8th International Conference on Information and

Communication Systems (ICICS), pp. 64–69, IEEE, Irbid,

Jordan, April 2017.

[33] A. A. Altowayan and L. Tao, “Word embeddings for Arabic

sentiment analysis,” in Proceedings of the 2016 IEEE Inter-

national Conference on Big Data (Big Data), pp. 3820–3825,

IEEE, Washington, DC, USA, December 2016.


[34] A. S. Alqarafi, A. Adeel, M. Gogate, K. Dashitpour,

A. Hussain, and T. Durrani, “Toward’s Arabic multi-modal

sentiment analysis,” in Proceedings of the International

Conference in Communications, Signal Processing, and Sys-

tems, pp. 2378–2386, Springer, Harbin, China, July 2017.


[35] A. Aliane, H. Aliane, M. Ziane, and N. Bensaou, “A genetic

algorithm feature selection based approach for Arabic sen-

timent classification,” in Proceedings of the ACS 13th In-

ternational Conference of Computer Systems and Applications

(AICCSA), pp. 1–6, IEEE, Agadir, Morocco, November 2016.


[36] N. F. B. Hathlian and A. M. Hafezs, “Sentiment-subjective

analysis framework for Arabic social media posts,” in Pro-

ceedings of the Saudi International Conference on Informa-

tion Technology (Big Data Analysis) (KACSTIT), pp. 1–6,

IEEE, Riyadh, Saudi Arabia, November 2016.


[37] A. Shoukry and A. Rafea, “A hybrid approach for sentiment

classification of egyptian dialect tweets,” in Proceedings of the

2015 First International Conference on Arabic Computational

Linguistics (ACLing), pp. 78–85, IEEE, Cairo, Egypt, 2015.


[38] A. Y. Al-Obaidi and V. W. Samawi, “Opinion mining:

analysis of comments written in Arabic colloquial,” in

Proceedings of the World Congress on Engineering and

Computer Science, vol. 1, London, UK, November 2016.


[39] T. H. A. Soliman, M. A. M. A. R. Hedar, M. Ali, and M. Doss,

“Mining social networks’ Arabic slang comments,” in Pro-

ceedings of IADIS European Conference on Data Mining,

vol. 22, p. 24, Prague, Czech Republic, July 2013.


[40] A. Hamdi, K. Shaban, and A. Zainal, “A class-specific sen-

timent analysis framework,” ACM Transactions on Asian and

Low-Resource Language Information Processing (TALLIP),

vol. 17, no. 4, p. 32, 2018.


[41] M. M. Altawaier and S. Tiun, “Comparison of machine

learning approaches on Arabic twitter sentiment analysis,”

International Journal on Advanced Science, Engineering and

Information Technology, vol. 6, no. 6, pp. 1067–1073, 2016.


[42] H. K. Aldayel and A. M. Azmi, “Arabic tweets sentiment

analysis-a hybrid scheme,” Journal of Information Science,

vol. 42, no. 6, pp. 782–797, 2016.


[43] S. Al-Azani and E.-S. M. El-Alfy, “Combining emojis with

Arabic textual features for sentiment classification,” in

Proceedings of the 2018 9th International Conference on

Information and Communication Systems (ICICS), pp. 139–

144, IEEE, Irbid, Jordan, April 2018.


[44] A. Elnagar, O. Einea, and L. Lulu, “Comparative study of

sentiment classification for automated translated Latin re-

views into Arabic,” in Proceedings of the 2017 IEEE/ACS 14th

International Conference on Computer Systems and Appli-

cations (AICCSA), pp. 443–448, IEEE, Hammamet, Tunisia,

October 2017.


[45] A. A. Altowayan and A. Elnagar, “Improving Arabic sen-

timent analysis with sentiment-specific embeddings,” in

Proceedings of the 2017 IEEE International Conference on Big

Data (Big Data), pp. 4314–4320, IEEE, Boston, MA, USA,

December 2017.


[46] S. Abuelenin, S. Elmougy, and E. Naguib, “Twitter sentiment

analysis for Arabic tweets,” in Proceedings of the Interna-

tional Conference on Advanced Intelligent Systems and

Informatics, pp. 467–476, Springer, Cairo, Egypt, September

2017.


[47] A. Mahmoud and T. Elghazaly, “Using twitter to monitor

political sentiment for Arabic slang,” in Intelligent Natural

Language Processing: Trends and Applications, pp. 53–66,

Springer, Berlin, Germany, 2018.


[48] A. Elouardighi, M. Maghfour, and H. Hammia, “Collecting

and processing Arabic facebook comments for sentiment

analysis,” in Proceedings of the International Conference on

Model and Data Engineering, pp. 262–274, Springer, Bar-

celona, Spain, October 2017.


[49] A.-K. Al-Tamimi, A. Shatnawi, and E. Bani-Issa, “Arabic

sentiment analysis of youtube comments,” in Proceedings of

the 2017 IEEE Jordan Conference on Applied Electrical En-

gineering and Computing Technologies (AEECT), pp. 1–6,

IEEE, Amman, Jordan, October 2017.


[50] H. AL-Rubaiee, R. Qiu, K. Alomar, and D. Li, “Techniques

for improving the labelling process of sentiment analysis in

the saudi stock market,” International Journal of Advanced

Computer Science and Applications, vol. 9, no. 3, pp. 34–43,

2018.


[51] H. Abdellaoui and M. Zrigui, “Using tweets and emojis to

build tead: an Arabic dataset for sentiment analysis,”

Computacion Y Sistemas  ́ , vol. 22, no. 3, 2018.


[52] F. H. H. Mahyoub, M. A. Siddiqui, and M. Y. Dahab,

“Building an Arabic sentiment lexicon using semi-supervised

learning,” Journal of King Saud University-Computer and

Information Sciences, vol. 26, no. 4, pp. 417–424, 2014.


[53] N. El-Naggar, Y. El-Sonbaty, and M. A. El-Nasr, “Sentiment

analysis of modern standard Arabic and egyptian dialectal

Arabic tweets,” in Proceedings of the Computing Conference,

pp. 880–887, IEEE, London, UK, July 2017.


[54] G. Alwakid, T. Osman, and T. Hughes-Roberts, “Challenges

in sentiment analysis for Arabic social networks,” Procedia

Computer Science, vol. 117, pp. 89–100, 2017.


[55] M. Biltawi, G. Al-Naymat, and S. Tedmori, “Arabic senti-

ment classification: a hybrid approach,” in Proceedings of the

2017 International Conference on New Trends in Computing

Sciences (ICTCS), pp. 104–108, IEEE, Amman, Jordan,

October 2017.


[56] R. Bouchlaghem, A. Elkhelifi, and R. Faiz, “Sentiment

analysis in Arabic twitter posts using supervised methods

with combined features,” in Proceedings of the International

Conference on Intelligent Text Processing and Computational

Linguistics, pp. 320–334, Springer, Konya, Turkey, April

2016.


[57] Y. AlMurtadha, “Mining trending hash tags for Arabic

sentiment analysis,” International Journal of Advanced

Computer Science and Applications, vol. 9, no. 2, pp. 189–194,

2018.


[58] A. AL-Saffar, B. Sabri, H. Tao, S. Awang, M. Abdul Majid,

and W. Al Saiagh, “Sentiment analysis in Arabic social media

using association rule mining,” Journal of Engineering and

Applied Sciences, vol. 11, pp. 3239–3247, 2016.


[59] H. Rahab, A. Zitouni, and M. Djoudi, “SIAAC: sentiment

polarity identification on Arabic algerian newspaper com-

ments,” in Proceedings of the Computational Methods in

Systems and Software, pp. 139–149, Springer, Szczecin,

Poland, September 2017.


[60] N. Boudad, R. Faizi, R. (ami, and R. Chiheb, “Sentiment

classification of Arabic tweets: a supervised approach,”

Journal of Mobile Multimedia, vol. 13, no. 3-4, pp. 233–243,

2017.


[61] Q. A. Al-Radaideh and G. Y. Al-Qudah, “Application of

rough set-based feature selection for Arabic sentiment

analysis,” Cognitive Computation, vol. 9, no. 4, pp. 436–445,

2017.


[62] T. Elghazaly, A. Mahmoud, and H. A. Hefny, “Political

sentiment analysis using twitter data,” in Proceedings of the

International Conference on Internet of things and Cloud

Computing, p. 11, ACM, Cambridge, UK, March 2016.


[63] H. Awwad and A. Alpkocak, “Performance comparison of

different lexicons for sentiment analysis in Arabic,” in

Proceedings of the 2016 @ird European Network Intelligence

Conference (ENIC), pp. 127–133, IEEE, Wroclaw, Poland,

September 2016.


[64] A. Bayoudhi, H. Ghorbel, and L. H. Belguith, “Sentiment

classification of Arabic documents: experiments with multi-

type features and ensemble algorithms,” in Proceedings of the

29th Pacific Asia Conference on Language, Information and

Computation, pp. 196–205, Shanghai, China, October 2015.


[65] O. El Ariss and L. M. Alnemer, “Morphology based Arabic

sentiment analysis of book reviews,” in Proceedings of the

International Conference on Computational Linguistics and

Intelligent Text Processing, pp. 115–128, Springer, Budapest,

Hungary, April 2017.


[66] R. Baly, H. Hajj, N. Habash, K. B. Shaban, and W. El-Hajj, “A

sentiment treebank and morphologically enriched recursive

deep models for effective sentiment analysis in Arabic,” ACM

Transactions on Asian and Low-Resource Language Infor-

mation Processing (TALLIP), vol. 16, no. 4, p. 23, 2017.


[67] M. Maghfour and A. Elouardighi, “Standard and dialectal

Arabic text classification for sentiment analysis,” in Pro-

ceedings of the International Conference on Model and Data

Engineering, pp. 282–291, Springer, Marrakesh, Morocco,

October 2018.


[68] K. M. Alomari, H. M. ElSherif, and K. Shaalan, “Arabic

tweets sentimental analysis using machine learning,” in

Proceedings of the International Conference on Industrial,

Engineering and Other Applications of Applied Intelligent

Systems, pp. 602–610, Springer, Arras, France, October 2017.


[69] S. Sabih, A. Sallam, and G. S. El-Taweel, “Manipulating

sentiment analysis challenges in morphological rich lan-

guages,” in Proceedings of the International Conference on

Advanced Intelligent Systems and Informatics, pp. 429–439,

Springer, Cairo, Egypt, September 2017.


[70] R. M. Duwairi and I. Qarqaz, “Arabic sentiment analysis

using supervised classification,” in Proceedings of the 2014

International Conference on Future Internet of @ings and

Cloud (FiCloud), pp. 579–583, IEEE, Rome, Italy, August

2014.


[71] S. Atia and K. Shaalan, “Increasing the accuracy of opinion

mining in Arabic,” in Proceedings of the 2015 First Inter-

national Conference on Arabic Computational Linguistics

(ACLing), pp. 106–113, IEEE, Konya, Turkey, April 2015.


[72] R. M. Duwairi, “Sentiment analysis for dialectical Arabic,” in

Proceedings of the 2015 6th International Conference on

Information and Communication Systems (ICICS), pp. 166–

170, IEEE, Amman, Jordan, April 2015.


[73] W. A. Ahmed and A. M. El-Halees, “Arabic opinion mining

using parallel decision trees,” in Proceedings of the 2017

Palestinian International Conference on Information and

Communication Technology (PICICT), pp. 46–52, IEEE,

Gaza, Palestinian, May 2017.


[74] R. M. Duwairi and I. Qarqaz, “A framework for Arabic

sentiment analysis using supervised classification,”

International Journal of Data Mining, Modelling and Man-

agement, vol. 8, no. 4, pp. 369–381, 2016.


[75] J. Akaichi, “Sentiment classification at the time of the

tunisian uprising: machine learning techniques applied to a

new corpus for Arabic language,” in 2014 European Network

Intelligence Conference (ENIC), pp. 38–45, IEEE, Wroclaw,

Poland, September 2014.


[76] Q. A. Al-Radaideh and L. M. Twaiq, “Rough set theory for

Arabic sentiment classification,” in Proceedings of the 2014

International Conference on Future Internet of @ings and

Cloud (FiCloud), pp. 559–564, IEEE, Rome, Italy, August

2014.


[77] J. M. Perea-Ortega, M. T. Mart ́ın-Valdivia, L. A. Ureña-

Lopez, and E. Mart  ́  ́ınez-Camara, “Improving polarity clas-  ́

sification of bilingual parallel corpora combining machine

learning and semantic orientation approaches,” Journal of

the American Society for Information Science and Technology,

vol. 64, no. 9, pp. 1864–1877, 2013.


[78] A. Mountassir, H. Benbrahim, and I. Berraba, “Sentiment

classification on Arabic corpora. A preliminary cross-study,”

Document Num ́erique, vol. 16, no. 1, pp. 73–96, 2013.


[79] A. Elnagar, Y. S. Khalifa, and A. Einea, “Hotel Arabic-reviews

dataset construction for sentiment analysis applications,” in

Intelligent Natural Language Processing: Trends and Appli-

cations, pp. 35–52, Springer, Berlin, Germany, 2018.


[80] A. El-Halees and A. Al-Asmar, “Ontology based Arabic

opinion mining,” Journal of Information & Knowledge

Management, vol. 16, no. 3, p. 1750028, 2017.


[81] A. M. Mostafa, “An automatic lexicon with exceptional-

negation algorithm for Arabic sentiments using supervised

classification,” Journal of @eoretical & Applied Information

Technology, vol. 95, no. 15, 2017.


[82] A. Nuseir, M. Al-Ayyoub, M. Al-Kabi, G. Kanaan, and R. Al-

Shalabi, “Improved hierarchical classifiers for multi-way

sentiment analysis,” International Arab Journal of Infor-

mation Technology (IAJIT), vol. 14, 2017.


[83] T. Al-Moslmi, M. Albared, A. Al-Shabi, N. Omar, and

S. Abdullah, “Arabic senti-lexicon: constructing publicly

available language resources for Arabic sentiment analysis,”

Journal of Information Science, vol. 44, no. 3, pp. 345–362,

2018.


[84] L. Abd-Elhamid, D. Elzanfaly, and A. S. Eldin, “Feature-

based sentiment analysis in online Arabic reviews,” in

Proceedings of the 2016 11th International Conference on

Computer Engineering & Systems (ICCES), pp. 260–265,

IEEE, Cairo, Egypt, December 2016.


[85] A. R. Hedar and M. Doss, “Mining social networks Arabic

slang comments,” in Proceedings of the IEEE Symposium on

Computational Intelligence and Data Mining (CIDM), Sin-

gapore, April 2013.


[86] M. El-Masri, N. Altrabsheh, H. Mansour, and A. Ramsay, “A

web-based tool for Arabic sentiment analysis,” Procedia

Computer Science, vol. 117, pp. 38–45, 2017.


[87] W. Cherif, A. Madani, and M. Kissi, “A new modeling

approach for Arabic opinion mining recognition,” in Pro-

ceedings of the 2015 Intelligent Systems and Computer Vision

(ISCV), pp. 1–6, IEEE, Fez, Morocco, March 2015.


[88] A. Barhoumi, Y. E. C. Aloulou, and L. H. Belguith, “Doc-

ument embeddings for Arabic sentiment analysis,” in Pro-

ceedings of the Conference on Language Processing and

Knowledge Management, Sfax, Tunisia, September 2017.


[89] W. Cherif, A. Madani, and M. Kissi, “A combination of low-

level light stemming and support vector machines for the

classification of Arabic opinions,” in Proceedings of the 2016

International Journal of Data Mining, Modelling and Man-

agement, vol. 8, no. 4, pp. 369–381, 2016.

11th International Conference on Intelligent Systems: @eories

and Applications (SITA), pp. 1–5, IEEE, Mohammedia,

Morocco, October 2016.


[90] S. Al-Azani and E.-S. M. El-Alfy, “Hybrid deep learning for

sentiment polarity determination of Arabic microblogs,” in

Proceedings of the International Conference on Neural In-

formation Processing, pp. 491–500, Springer, Guangzhou,

China, November 2017.


[91] M. Abbes, Z. Kechaou, and A. M. Alimi, “Enhanced deep

learning models for sentiment analysis in arab social media,”

in Proceedings of the International Conference on Neural

Information Processing, pp. 667–676, Springer, Guangzhou,

China, November 2017.


[92] B. Brahimi, M. Touahria, and A. Tari, “Data and text mining

techniques for classifying Arabic tweet polarity,” Journal of

Digital Information Management, vol. 14, no. 1, 2016.


[93] I. Touati, M. Graja, M. Ellouze, and L. H. Belguith, “Towards

Arabic semantic opinion mining: identifying opinion, po-

larity and intensity,” in Proceedings of the Mediterranean

Conference on Pattern Recognition and Artificial Intelligence,

pp. 131–136, ACM, Tebessa, Algeria, November 2016.


[94] M. A. Sghaier and M. Zrigui, “Sentiment analysis for Arabic

e-commerce websites,” in Proceedings of the International

Conference on Engineering & MIS (ICEMIS), pp. 1–7, IEEE,

Agadir, Morocco, 2016.


[95] W. A. Hussien, Y. M. Tashtoush, M. Al-Ayyoub, and

M. N. Al-Kabi, “Are emoticons good enough to train

emotion classifiers of Arabic tweets?,” in Proceedings of the

7th International Conference on Computer Science and In-

formation Technology (CSIT), pp. 1–6, IEEE, Helsinki, Fin-

land, June 2016.


[96] R. T. Khasawneh, H. A. Wahsheh, I. M. Alsmadi, and

M. N. AI-Kabi, “Arabic sentiment polarity identification

using a hybrid approach,” in Proceedings of the 2015 6th

International Conference on Information and Communica-

tion Systems (ICICS), pp. 148–153, IEEE, Amman, Jordan,

April 2015.


[97] R. Bouchlaghem, A. Elkhelifi, and R. Faiz, “SVM based

approach for opinion classification in Arabic written tweets,”

in Proceedings of the 2015 IEEE/ACS 12th International

Conference of Computer Systems and Applications (AICCSA),

pp. 1–4, IEEE, Marrakech, Morocco, November 2015.


[98] A. E. Rihab, B. Faiz, and Rim, “A machine learning approach

for classifying sentiments in Arabic tweets,” in Proceedings of

the 6th International Conference on Web Intelligence, Mining

and Semantics, p. 24, ACM, Nˆımes, France, June 2016.


[99] S. Ismail, A. Alsammak, and T. Elshishtawy, “A generic

approach for extracting aspects and opinions of Arabic re-

views,” in Proceedings of the 10th International Conference on

Informatics and Systems, pp. 173–179, ACM, Cairo, Egypt,

May 2016.


[100] M. Hammad and M. Al-awadi, “Sentiment analysis for

Arabic reviews in social networks using machine learning,”

in Information Technology: New Generations, pp. 131–139,

Springer, Berlin, Germany, 2016.


[101] W. Adouane and R. Johansson, “Gulf Arabic linguistic re-

source building for sentiment analysis,” in Proceedings of the

2016 International Conference on Language Resources and

Evaluation, Portoroz, Slovenia, May 2016. ˇ


[102] L. Al-Horaibi and M. B. Khan, “Sentiment analysis of Arabic

tweets using text mining techniques,” in Proceedings of the

First International Workshop on Pattern Recognition,

vol. 10011, International Society for Optics and Photonics,

Tokyo, Japan, May 2016.


[103] N. Al-Twairesh, H. Al-Khalifa, and A. AlSalman, “Arasenti:

large-scale twitter-specific Arabic sentiment lexicons,” in

Proceedings of the 54th Annual Meeting of the Association for

Computational Linguistics, vol. 1, pp. 697–705, Berlin,

Germany, August 2016.


[104] H. Al-Rubaiee, R. Qiu, K. Alomar, and D. Li, “Sentiment

analysis of Arabic tweets in e-learning,” Journal of Computer

Science, vol. 12, no. 11, pp. 553–563, 2016.


[105] B. Al Shboul, M. Al-Ayyoub, and Y. Jararweh, “Multi-way

sentiment classification of Arabic reviews,” in Proceedings of

the 2015 6th International Conference on Information and

Communication Systems (ICICS), pp. 206–211, IEEE,

Amman, Jordan, April 2015.


[106] M. Alhazmi and N. Salim, “Arabic opinion target extraction

from tweets,” ARPN Journal of Engineering and Applied

Sciences, vol. 10, no. 3, pp. 1023–1026, 2015.


[107] S. Al-Osaimi and K. M. Badruddin, “Role of emotion icons in

sentiment classification of Arabic tweets,” in Proceedings of

the 6th International Conference on Management of Emergent

Digital Ecosystems, pp. 167–171, ACM, Buraidah Al Qassim,

Saudi Arabia, September 2014.


[108] E. Refaee, “Sentiment analysis for micro-blogging platforms

in Arabic,” in Proceedings of the International Conference on

Social Computing and Social Media, pp. 275–294, Springer,

Vancouver, Canada, July 2017.


[109] S. Al-Azani and E.-S. M. El-Alfy, “Imbalanced sentiment

polarity detection using emoji-based features and bagging

ensemble,” in Proceedings of the 2018 1st International

Conference on Computer Applications & Information Security

(ICCAIS), pp. 1–5, IEEE, Riyadh, Saudi Arabia, April 2018.


[110] A. El Ali, T. C. Stratmann, S. Park, J. Schoning, W. Heuten,  ̈

and S. C. Boll, “Measuring, understanding, and classifying

news media sympathy on twitter after crisis events,” in

Proceedings of the 2018 CHI Conference on Human Factors in

Computing Systems, p. 556, ACM, Montreal, Canada, April

2018.


[111] M. Al-Batah, S. Mrayyen, and M. Alzaqebah, “Investigation

of naive bayes combined with multilayer perceptron for

Arabic sentiment analysis and opinion mining,” Journal of

Computer Science, vol. 14, pp. 1104–1114, 01 2018.


[112] A. M. Alayba, V. Palade, M. England, and R. Iqbal, “A

combined CNN and LSTM model for Arabic sentiment

analysis,” in Proceedings of the International Cross-Domain

Conference for Machine Learning and Knowledge Extraction,

pp. 179–191, Springer, Hamburg, Germany, August 2018.


[113] M. Al-Smadi, M. Al-Ayyoub, Y. Jararweh, and

O. Qawasmeh, “Enhancing aspect-based sentiment analysis

of Arabic hotels’ reviews using morphological, syntactic and

semantic features,” Information Processing & Management,

Springer, Berlin, Germany, 2018.


[114] M. Gridach, H. Haddad, and H. Mulki, “Empirical evaluation

of word representations on Arabic sentiment analysis,” in

Proceedings of the International Conference on Arabic Lan-

guage Processing, pp. 147–158, Springer, Fez, Morocco,

October 2017.


[115] S. Al-Azani and E.-S. M. El-Alfy, “Using word embedding

and ensemble learning for highly imbalanced data sentiment

analysis in short Arabic text,” Procedia Computer Science,

vol. 109, pp. 359–366, 2017.


[116] M. Abdul-Mageed, “Modeling arabic subjectivity and sen-

timent in lexical space,” Information Processing & Man-

agement, vol. 56, no. 2, pp. 291–307, 2017.


[117] S. Siddiqui, A. A. Monem, and K. Shaalan, “Towards im-

proving sentiment analysis in Arabic,” in Proceedings of the

International Conference on Advanced Intelligent Systems

and Informatics, pp. 114–123, Springer, Cairo, Egypt, Oc-

tober 2016.


[118] T. Khalil, A. Halaby, M. Hammad, and S. R. El-Beltagy,

“Which configuration works best? an experimental study on

supervised Arabic twitter sentiment analysis,” in Proceedings

of the 2015 First International Conference on Arabic Com-

putational Linguistics (ACLing), pp. 86–93, IEEE, Cairo,

Egypt, April 2015.


[119] S. Siddiqui, A. A. Monem, and K. Shaalan, “Sentiment

analysis in Arabic,” in Proceedings of the International

Conference on Applications of Natural Language to Infor-

mation Systems, pp. 409–414, Springer, Salford, UK, June

2016.


[120] M. Nabil, M. Aly, and A. Atiya, “ASTD: Arabic sentiment

tweets dataset,” in Proceedings of the 2015 Conference on

Empirical Methods in Natural Language Processing,

pp. 2515–2519, Lisbon, Portugal, September 2015.


[121] A. Ziani, N. Azizi, and Y. T. Guiyassa, “Combining random

sub space algorithm and support vector machines classifier

for Arabic opinions analysis,” in Advanced Computational

Methods for Knowledge Engineering, pp. 175–184, Springer,

Berlin, Germany, 2015.


[122] H. ElSahar and S. R. El-Beltagy, “Building large Arabic multi-

domain resources for sentiment analysis,” in Proceedings of

the International Conference on Intelligent Text Processing

and Computational Linguistics, pp. 23–34, Springer, Cairo,

Egypt, April 2015.


[123] N. Omar, M. Albared, T. Al-Moslmi, and A. Al-Shabi, “A

comparative study of feature selection and machine learning

algorithms for Arabic sentiment classification,” in Proceed-

ings of the Asia information retrieval symposium, pp. 429–

443, Springer, Sarawak, Malaysia, June 2014.


[124] A. S. Al-Subaihin and H. S. Al-Khalifa, “A system for sen-

timent analysis of colloquial Arabic using human compu-

tation,” @e Scientific World Journal, vol. 2014, Article ID

631394, 8 pages, 2014.


[125] M. Aly and A. Atiya, “LABR: a large scale Arabic book

reviews dataset,” in Proceedings of the 51st Annual Meeting of

the Association for Computational Linguistics, vol. 2,

pp. 494–498, Sofia, Bulgaria, August 2013.


[126] K. Khalifa and N. Omar, “A hybrid method using lexicon-

based approach and naive bayes classifier for Arabic opinion

question answering,” Journal of Computer Science, vol. 10,

no. 10, pp. 1961–1968, 2014.


[127] S. O. Alhumoud, M. I. Altuwaijri, T. M. Albuhairi, and

W. M. Alohaideb, “Survey on Arabic sentiment analysis in

twitter,” International Science Index, vol. 9, no. 1, pp. 364–

368, 2015.


[128] Y. Yoshida, T. Hirao, T. Iwata, M. Nagata, and

Y. Matsumoto, “Transfer learning for multiple-domain

sentiment analysis-identifying domain dependent/indepen-

dent word polarity,” in Proceedings of the Twenty-Fifth AAAI

Conference on Artificial Intelligence AAAI, San Francisco,

CA, USA, February 2011.


[129] S. Abdennadher, H. Ayman, C. Sabty, R. Salem, N. Tarhouny,

and S. Zohny, “Building a corpus to categorize Arabic short

text using games with a purpose,” in Proceedings of the 13th

International Conference WWW/Internet (ICWI), Porto,

Portugal, 2014.


[130] U. Kumar and P. Jaiswal, “Comparative Study on Sentiment

Analysis and Opinion Mining,” International Journal of

Engineering and Technology, IJET, vol. 8, no. 2, 2016.


[131] F. Hemmatian and M. K. Sohrabi, “a survey on classification

techniques for opinion mining and sentiment analysis,”

Artificial Intelligence Review, vol. 52, no. 3, pp. 1–51, 2017.


[132] N. A. Abdulla, N. A. Ahmed, M. A. Shehab, and M. Al-

Ayyoub, “Arabic sentiment analysis: lexicon-based and

corpus-based,” in Proceedings of the 2013 IEEE Jordan

Conference on Applied Electrical Engineering and Computing

Technologies (AEECT), pp. 1–6, IEEE, Amman, Jordan, 2013.


[133] M. Salameh, S. Mohammad, and S. Kiritchenko, “Sentiment

after translation: a case-study on Arabic social media posts,”

in Proceedings of the 2015 Conference of the North American

Chapter of the Association for Computational Linguistics:

Human Language Technologies, pp. 767–777, Denver, CO,

USA, June 2015.


[134] N. Farra and K. McKeown, “Smarties: sentiment models for

Arabic target entities,” 2017, https://arxiv.org/abs/1701.

03434.


[135] S. Al-Azani and E.-S. El-Alfy, “Emojis-based sentiment

classification of Arabic microblogs using deep recurrent

neural networks,” in Proceedings of the 2018 International

Conference on Computing Sciences and Engineering (ICCSE),

pp. 1–6, IEEE, Kuwait City, Kuwait, March 2018.





https://www.hindawi.com/journals/acisc/2020/7403128/


No comments: