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Full Length Article
Volume 2 , Issue 2, PP: 74-87 , 2020

Title

A Survey on Sentiment Analysis Algorithms and Techniques For Arabic Textual Data

Authors Names :   Lamia Mohamed Ahmed   1     Gawaher Soliman Hussein   2     Abdel Nasser Hessin Zaied   3  

1  Affiliation :  Faculty of Computer and Informatics, Zagazige University, Sharkia, Egypt

    Email :  lamia.mohamed993@gmail.com


2  Affiliation :  Faculty of Computer and Informatics, Zagazige University, Sharkia, Egypt

    Email :  Gawaherahmed@yahoo.com


3  Affiliation :  Dept. Operations Research, Faculty of Computer and Informatics, Zagazige University, Sharkia, Egypt

    Email :  nasserhr@zu.edu.eg



Doi   :  10.5281/zenodo.4571565

Received April 04, 2020 Revised November 09, 2020 Accepted December 03, 2020

Abstract :

The concept Sentiment means the feeling, behavior, belief, or attitude towards something that almost being embedded. sentiment analysis is the process of analyzing, extracting, studying, and classifying the various reviews, opinions are given by people, and human’s emictions into positive, negative, neutral. It is considered one of the most significant scientific branches that aim to determine the behavior of the speaker, the attitude of the writer according to some topic, or the overall emotional reaction to website, document, event, interaction, products, or services. many users can share every day various opinions on different topics that may be detected or embedded by using micro-blogging which considered a rich resource for sentiment analysis and belief mining such as Facebook, Twitter, forums, and Blogs. recently a huge number of posted comments, tweets, and reviews of different social media websites include rich information in addition to most of the on-line shopping sites provide the opportunity to customers to write reviews about products in order to enhance the sales of those products and to improve both of product quality and customer satisfaction. manual analysis of these large reviews is practically impossible thus it is needed to discover an automated approach to solving such a hard process. In the Middle East and particularly in the Arab world, social media websites continue to be the top-visited websites especially with the current social and political changes in this part of the world. the main objective of that research is to differentiate between various algorithms and techniques of sentiment analysis and classification dependent on the Arabic language as a little number of researchers discusses that point relevant to the Arabic language. Different algorithms and techniques of data mining such as Support Vector Machine (SVM), Naïve Bayes (NB), Bayesian Network (BN), Decision tree (DT), k-nearest neighbor (KNN), Maximum Entropy (ME), and Neural Network (NN) in addition to many other alternative techniques which are used for analyzing and classifying textual data. For the reasons of difficulties in analyzing and mining a large number of linguistic words for their Those techniques are estimated based on the Arabic language due to its richness and diversity. The comparison between data mining techniques showed that the most accurate technique is the support vector machine (SVM) algorithm. every successful sentiment depends on two essential analysis tools are language and culture.

Keywords :

Sentiment Analysis , Arabic Sentiment Analysis , Arabic sentiment classification , Sentiment Structure Review , Arabic Dialects , Dialectical Lexicons , Sentiment Classification Model.

References :

 

1.      Mestry, P., Joshi, Sh., Mehta, S., & Save, A. (2017). A Survey on Twitter Sentiment Analysis with Various Algorith ms. International Journal of Computer Applications (0975–8887) The National Conference on Role of Engineers in National Building, pp.20-24.

2.      Boudad, N., Faizi, R., Thami, R. O. H., & Chiheb, R. (2018). Sentiment analysis in Arabic: A review of the literature. Ain Shams Engineering Journal, 9(4), 2479-2490.‏

3.      Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University Engineering Sciences, 30(4), 330-338.‏

4.      Huq, M. R., Ali, A., & Rahman, A. (2017). Sentiment analysis on Twitter data using KNN and SVM.   IJACSA) International Journal of Advanced Computer Science and Applications, 8(6), 19-25.‏

5.      Duwairi, R. M. (2015, April). Sentiment analysis for dialectical Arabic. In 2015 6th International Conference on Information and Communication Systems (ICICS) (pp. 166-170). IEEE.‏

6.      Perti, A., Trivedi, M. C., & Sinha, A. (2020). Development of intelligent model for twitter sentiment analysis. Materials Today: Proceedings.‏

7.      Filippo Chiarello, F., Bonaccorsi, A., Fantoni, G., Ossola, G., Cimino, A., & Dell'Orletta, F. (2018). Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media. In 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018). Proceedings.‏

8.      Oueslati, O., Cambria, E., HajHmida, M. B., & Ounelli, H. A review of sentiment analysis research in Arabic language. ScienceDirect, Future Generation Computer Systems 112 (2020) (pp. 408–430)

9.      Elarnaoty, M., AbdelRahman, S., & Fahmy, A. (2012). A machine learning approach for opinion holder extraction in Arabic language. arXiv preprint arXiv:1206.1011.‏

10.   Abdulla, N. A., Ahmed, N. A., Shehab, M. A., & Al-Ayyoub, M. (2013, December). Arabic sentiment analysis: Lexicon-based and corpus-based. In 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT) (pp. 1-6). IEEE.‏

11.   Mostafa, A. M. (2017). AN AUTOMATIC LEXICON WITH EXCEPTIONAL-NEGATION ALGORITHM FOR ARABIC SENTIMENTS USING SUPERVISED CLASSIFICATION. Journal of Theoretical & Applied Information Technology, 95(15).‏

12.   Fang, Y., Tan, H., & Zhang, J. (2018). Multi-strategy sentiment analysis of consumer reviews based on semantic fuzziness. Ieee Access, 6, 20625-20631.‏

13.   Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2019). Social information discovery enhanced by sentiment analysis techniques. Future Generation Computer Systems, 95, 816-828.‏

14.   Araque, O., Zhu, G., & Iglesias, C. A. (2019). A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems, 165, 346-359.‏

15.   Manni, A., Jaiswal, N., & Jaiswal, N. (2017). Product Rating Based On Review Using Data Mining. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3), 118-121.‏

16.   Ankitkumar, D., Badre, R., R., Kinikar, M. (2014, November). International Journal of Innovative, International Journal of Innovative Research in Computer and Communication Engineering, 6, 6633- 6639

17.   Yukun Ma, Haiyun Peng, Erik Cambria, Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM, in: AAAI, 2018, pp. 5876–5883.

18.   Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.‏

19.   Anjanabhargavi A. Kulkarni1,Vidyashree A. Hundekar2,S. S. Sannakki3,Vijay S., Rajpurohit4. (2017, June). Survey on Opinion Mining Algorithms and Applications, International Journal of Computer Techniques, pp. 2394-2231

20.   Mostafa, A. M. (2017). An evaluation of sentiment analysis and classification algorithms for Arabic  textual data. Int. J. Comput. Appl, 158(3), 1-8.‏

21.   Kariman Elshakankery, Mona F. Ahmed, HILATSA: A hybrid incremental learning approach for Arabic tweets sentiment analysis, Egyptian Inform. J. (2019).

22.   Gamal, D., Alfonse, M., Salem, A.M., 2019. Twitter Benchmark Dataset for Arabic Sentiment Analysis. IJ Mod Educ Comput Sci 11, 33–38. https://doi.org/10.5815/ijmecs.2019.01.04

23.   Ziani, A., Zenakhra, D., Cheriguene, S., Aldwairi, M., 2019. Combining RSS-SVM with Genetic Algorithm for Arabic Opinions Analysis. Int J Intell Syst Technol Appl 18, 152–178.

24.   Rahab, H., Zitouni, A., Djoudi, M., 2018. SIAAC: Sentiment Polarity Identification on Arabic Algerian Newspaper Comments, Applied Computational Intelligence and Mathematical Methods.  https://doi.org/10.1007/978-3- 319-67621-0

25.   Nora Al-Twairesh, Hend Al-Khalifa, AbdulMalik Alsalman, Yousef Al-Ohali, Sentiment analysis of arabic tweets: Feature engineering and a hybrid approach, 2018, arXiv preprint arXiv:1805.08533.

26.   Mohcine Maghfour, Abdeljalil Elouardighi, Standard and dialectal Arabic text classification for sentiment analysis, in: International Conference on Model and Data Engineering, Springer, 2018, pp. 282–291.

27.   Khaled Mohammad Alomari, Hatem M. ElSherif, Khaled Shaalan, Arabic tweets sentimental analysis using machine learning, in: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, 2017, pp. 602–610.

28.   Mariam Biltawi, Ghazi Al-Naymat, Sara Tedmori, Arabic sentiment classification: A hybrid approach, in: 2017 International Conference on New Trends in Computing Sciences, ICTCS, IEEE, 2017, pp. 104–108.

29.   Lamia Al-Horaibi, Muhammad Badruddin Khan, Sentiment analysis of arabic tweets using text mining techniques, in: First International Workshop on Pattern Recognition, Vol. 10011, International Society for Optics and Photonics, 2016, p. 100111F.

30.   Mustafa Hammad, Mouhammd Al-awadi, Sentiment analysis for arabic reviews in social networks using machine learning, in: Information Technology: New Generations, Springer, 2016, pp. 131–139.

31.   Hamed Al-Rubaiee, Renxi Qiu, Dayou Li, Identifying mubasher software products through sentiment analysis of arabic tweets, in: Industrial Informatics and Computer Systems (CIICS), 2016 International Conference on, IEEE, 2016, pp. 1–6.

32.   Amine Bayoudhi, Hatem Ghorbel, Lamia Hadrich 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, 2015, pp. 196–205.

33.   Hossam S. Ibrahim, Sherif M. Abdou, Mervat Gheith, Sentiment analysis for modern standard Arabic and colloquial, 2015, arXiv preprint arXiv: 1505.03105.

34.   Mahmoud Nabil, Mohamed A. Aly, Amir F. Atiya, ASTD: Arabic sentiment tweets dataset, in: EMNLP, 2015, pp. 2515–2519.

35.   Mahyoub, F., Siddiqui, M., and Dahab, M. “Building an arabic sentiment lexicon using semi-supervised learning,” Journal of King Saud University–Computer and Information Sciences, vol. 26, p. 417-424, 2014

36.   Faqeeh, M., Abdulla, N., Al-Ayyoub, Y., and Quwaider, M. “Cross-lingual short-text document classification for Facebook comments,” IEEE International Conference on Future Internet of Things and Cloud, p.573-578, 2014

37.   Akaichi, J. “Sentiment classification at the time of the Tunisian uprising,” IEEE International European Conference on Network Intelligence, pp.38-45, 2014

38.   Nawaf A. Abdulla, Mahmoud Al-Ayyoub, Mohammed Naji Al-Kabi, An extended analytical study of arabic sentiments, Int. J. Big Data Intell. 1 (1–2) (2014) 103–113.

39.   Rehab Duwairi, Mahmoud El-Orfali, A study of the effects of preprocessing strategies on sentiment analysis for Arabic text, J. Inf. Sci. 40 (4) (2014) 501–513.

40.   Muhammad Abdul-Mageed, Mona Diab, Sandra Kübler, SAMAR: Subjectivity and sentiment analysis for  arabic social media, Comput. Speech Lang. 28 (1) (2014) 20–37.