Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review
Received: 5 January 2023 | Revised: 22 January 2023 and 24 January 2023 | Accepted: 26 January 2023 | Online: 26 February 2023
Corresponding author: Tahani Alqurashi
Abstract
Social media platforms have a huge impact on our daily lives. They have succeeded in attracting many people to spend time communicating and expressing themselves. Twitter is a social media platform that could be considered as a source of public opinion about products, services, and events. Sentiment analysis is the art of studying public feelings about certain topics, which may be positive, negative, or neutral. This paper provides a systematic review of Arabic tweet sentiment analysis on papers published from 2012 to 2021 in digital libraries including IEEE Explorer, Science Direct, Springer Link, and Google Scholar. The main aim of this systematic review is to investigate the trends in the topics reported and to highlight potential new research lines. To achieve that, three main stages were implemented: planning, conducting, and reporting the review. Our findings suggest the need for an open-source large Arabic tweet dataset that can be used by researchers. Also, it was found that researchers have used various classification techniques, which led to different results.
Keywords:
arabic sentiment analysis, systematic review, social media, twitterDownloads
References
"Twitter global mDAU 2022," Statista, Nov. 11, 2022. https://www.statista.com/statistics/970920/monetizable-daily-active-twitter-users-worldwide/.
A. M. Alayba, V. Palade, M. England, and R. Iqbal, "Arabic language sentiment analysis on health services," in 1st International Workshop on Arabic Script Analysis and Recognition, Nancy, France, Apr. 2017, pp. 114–118. DOI: https://doi.org/10.1109/ASAR.2017.8067771
"World Arabic Language Day 2020: focus on Arabic Language Academies | UNESCO." https://www.unesco.org/en/articles/world-arabic-language-day-2020-focus-arabic-language-academies (accessed Jan. 28, 2023).
N. Boudad, R. Faizi, R. Oulad Haj Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature," Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2479–2490, Dec. 2018.
B. Kitchenham, "Procedures for Performing Systematic Reviews," Keele University, Keele, UK, Technical Report TR/SE-0401, 2004.
M. Salameh, S. Mohammad, and S. Kiritchenko, "Sentiment after Translation: A Case-Study on Arabic Social Media Posts," in Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA, Jun. 2015, pp. 767–777. DOI: https://doi.org/10.3115/v1/N15-1078
D. Kurniawati, E. Prayitno, D. F. Sari, and S. N. Putra, "Sentiment Analysis of Twitter Use on Policy Institution Services using Naive Bayes Classifier Method," Journal of International Conference Proceedings, vol. 2, no. 1, Apr. 2019, Art. no. 33. DOI: https://doi.org/10.32535/jicp.v2i1.409
N. Boudad, R. Faizi, R. Oulad Haj Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature," Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2479–2490, Dec. 2018. DOI: https://doi.org/10.1016/j.asej.2017.04.007
E. Refaee and V. Rieser, "Benchmarking Machine Translated Sentiment Analysis for Arabic Tweets," in Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA, Jun. 2015, pp. 71–78. DOI: https://doi.org/10.3115/v1/N15-2010
H. M. Najadat, A. A. Alzu’bi, F. Shatnawi, S. Rawashdeh, and W. Eyadat, "Analyzing Social Media Opinions Using Data Analytics," in 11th International Conference on Information and Communication Systems, Irbid, Jordan, Apr. 2020, pp. 266–271. DOI: https://doi.org/10.1109/ICICS49469.2020.239497
R. M. Alahmary, H. Z. Al-Dossari, and A. Z. Emam, "Sentiment Analysis of Saudi Dialect Using Deep Learning Techniques," in International Conference on Electronics, Information, and Communication, Auckland, New Zealand, Jan. 2019, pp. 1–6. DOI: https://doi.org/10.23919/ELINFOCOM.2019.8706408
M. A. Alanezi and N. M. Hewahi, "Tweets Sentiment Analysis During COVID-19 Pandemic," in International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, Sakheer, Bahrain, Oct. 2020, pp. 1–6. DOI: https://doi.org/10.1109/ICDABI51230.2020.9325679
D. Abuaiadah, D. Rajendran, and M. Jarrar, "Clustering Arabic Tweets for Sentiment Analysis," in 14th International Conference on Computer Systems and Applications, Hammamet, Tunisia, Nov. 2017, pp. 449–456. DOI: https://doi.org/10.1109/AICCSA.2017.162
G. Alwakid, T. Osman, and T. Hughes-Roberts, "Challenges in Sentiment Analysis for Arabic Social Networks," Procedia Computer Science, vol. 117, pp. 89–100, Jan. 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.097
S. Almouzini, M. khemakhem, and A. Alageel, "Detecting Arabic Depressed Users from Twitter Data," Procedia Computer Science, vol. 163, pp. 257–265, Jan. 2019. DOI: https://doi.org/10.1016/j.procs.2019.12.107
E. Alyami, S. Matwin, T. Elmasri, and H. Ali-Hassan, "Mining The Saudi Twittersphere for Expressive Support Towards Women," in Fourth World Conference on Smart Trends in Systems, Security and Sustainability, London, UK, Jul. 2020, pp. 301–305. DOI: https://doi.org/10.1109/WorldS450073.2020.9210367
A. Dahou, S. Xiong, J. Zhou, and M. A. Elaziz, "Multi-Channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification," ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 18, no. 4, pp. 1–23, Feb. 2019. DOI: https://doi.org/10.1145/3314941
M. Abdullah, M. Hadzikadicy, and S. Shaikhz, "SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN-LSTM Deep Learning," in 17th IEEE International Conference on Machine Learning and Applications, Orlando, FL, USA, Dec. 2018, pp. 835–840. DOI: https://doi.org/10.1109/ICMLA.2018.00134
S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko, "SemEval-2018 Task 1: Affect in Tweets," in 12th International Workshop on Semantic Evaluation, New Orleans, LA, USA, Jun. 2018, pp. 1–17. DOI: https://doi.org/10.18653/v1/S18-1001
M. Beseiso and H. Elmousalami, "Subword Attentive Model for Arabic Sentiment Analysis: A Deep Learning Approach," ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 19, no. 2, pp. 1–17, Oct. 2020. DOI: https://doi.org/10.1145/3360016
E. A. Abozinadah and J. H. Jones, "Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts," International Journal of Data Mining & Knowledge Management Process, vol. 6, no. 6, pp. 17–28, Nov. 2016. DOI: https://doi.org/10.5121/ijdkp.2016.6602
H. AlSalman, "An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media," in 3rd International Conference on Computer Applications & Information Security, Riyadh, Saudi Arabia, Mar. 2020, pp. 1–4. DOI: https://doi.org/10.1109/ICCAIS48893.2020.9096850
N. A. Abdulla, N. A. Ahmed, M. A. Shehab, and M. Al-Ayyoub, "Arabic sentiment analysis: Lexicon-based and corpus-based," in IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, Amman, Jordan, Dec. 2013, pp. 1–6. DOI: https://doi.org/10.1109/AEECT.2013.6716448
S. Alhumoud, T. Albuhairi, and W. Alohaideb, "Hybrid sentiment analyser for Arabic tweets using R," in 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Lisbon, Portugal, Nov. 2015, vol. 1, pp. 417–424. DOI: https://doi.org/10.5220/0005616204170424
S. R. El-Beltagy and A. Ali, "Open issues in the sentiment analysis of Arabic social media: A case study," in 9th International Conference on Innovations in Information Technology, Al Ain, United Arab Emirates, Mar. 2013, pp. 215–220. DOI: https://doi.org/10.1109/Innovations.2013.6544421
M. Abdul-Mageed, M. Diab, and S. Kubler, "SAMAR: Subjectivity and sentiment analysis for Arabic social media," Computer Speech & Language, vol. 28, no. 1, pp. 20–37, Jan. 2014. DOI: https://doi.org/10.1016/j.csl.2013.03.001
A. Assiri, A. Emam, and H. Al-Dossari, "Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis," Journal of Information Science, vol. 44, no. 2, pp. 184–202, Apr. 2018. DOI: https://doi.org/10.1177/0165551516688143
N. A. Abdulla, N. A. Ahmed, M. A. Shehab, M. Al-Ayyoub, M. N. Al-Kabi, and S. Al-rifai, "Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis," International Journal of Information Technology and Web Engineering, vol. 9, no. 3, pp. 55–71, Jul. 2014. DOI: https://doi.org/10.4018/ijitwe.2014070104
M. Al-Ayyoub, S. B. Essa, and I. Alsmadi, "Lexicon-based sentiment analysis of Arabic tweets," International Journal of Social Network Mining, vol. 2, no. 2, pp. 101–114, Jan. 2015. DOI: https://doi.org/10.1504/IJSNM.2015.072280
M. Biltawi, G. Al-Naymat, and S. Tedmori, "Arabic Sentiment Classification: A Hybrid Approach," in International Conference on New Trends in Computing Sciences, Amman, Jordan, Oct. 2017, pp. 104–108. DOI: https://doi.org/10.1109/ICTCS.2017.24
R. T. Khasawneh, H. A. Wahsheh, I. M. Alsmadi, and M. N. AI-Kabi, "Arabic sentiment polarity identification using a hybrid approach," in 6th International Conference on Information and Communication Systems, Amman, Jordan, Apr. 2015, pp. 148–153. DOI: https://doi.org/10.1109/IACS.2015.7103218
K. Elshakankery and M. F. Ahmed, "HILATSA: A hybrid Incremental learning approach for Arabic tweets sentiment analysis," Egyptian Informatics Journal, vol. 20, no. 3, pp. 163–171, Nov. 2019. DOI: https://doi.org/10.1016/j.eij.2019.03.002
S. Alzu’bi, O. Badarneh, B. Hawashin, M. Al-Ayyoub, N. Alhindawi, and Y. Jararweh, "Multi-Label Emotion Classification for Arabic Tweets," in Sixth International Conference on Social Networks Analysis, Management and Security, Granada, Spain, Oct. 2019, pp. 499–504. DOI: https://doi.org/10.1109/SNAMS.2019.8931715
O. Badarneh, M. Al-Ayyoub, N. Alhindawi, L. A. Tawalbeh, and Y. Jararweh, "Fine-Grained Emotion Analysis of Arabic Tweets: A Multi-target Multi-label Approach," in 12th International Conference on Semantic Computing, Laguna Hills, CA, USA, Feb. 2018, pp. 340–345. DOI: https://doi.org/10.1109/ICSC.2018.00070
D. E. Salhi, A. Tari, and M.-T. Kechadi, "Sentiment Analysis Application on Twitter for E-reputation," in 6th International Conference on Image and Signal Processing and their Applications, Mostaganem, Algeria, Nov. 2019, pp. 1–6. DOI: https://doi.org/10.1109/ISPA48434.2019.8966833
A. Baccouche, B. Garcia-Zapirain, and A. Elmaghraby, "Annotation Technique for Health-Related Tweets Sentiment Analysis," in International Symposium on Signal Processing and Information Technology, Louisville, KY, USA, Dec. 2018, pp. 382–387. DOI: https://doi.org/10.1109/ISSPIT.2018.8642685
R. Ismail, M. Omer, M. Tabir, N. Mahadi, and I. Amin, "Sentiment Analysis for Arabic Dialect Using Supervised Learning," in International Conference on Computer, Control, Electrical, and Electronics Engineering, Khartoum, Sudan, Dec. 2018, pp. 1–6. DOI: https://doi.org/10.1109/ICCCEEE.2018.8515862
M. E. M. Abo, N. A. K. Shah, V. Balakrishnan, and A. Abdelaziz, "Sentiment analysis algorithms: evaluation performance of the Arabic and English language," in International Conference on Computer, Control, Electrical, and Electronics Engineering, Khartoum, Sudan, Aug. 2018, pp. 1–5.
N. F. Alshammari and A. A. AlMansour, "Aspect-based Sentiment Analysis for Arabic Content in Social Media," in International Conference on Electrical, Communication, and Computer Engineering, Istanbul, Turkey, Jun. 2020, pp. 1–6. DOI: https://doi.org/10.1109/ICECCE49384.2020.9179327
N. F. Bin Hathlian and A. M. Hafezs, "Sentiment - subjective analysis framework for arabic social media posts," in 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT), Riyadh, Saudi Arabia, Nov. 2016, pp. 1–6. DOI: https://doi.org/10.1109/KACSTIT.2016.7756073
M. Alassaf and A. M. Qamar, "Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, Part A, pp. 2849–2859, Jun. 2022. DOI: https://doi.org/10.1016/j.jksuci.2020.10.023
H. Al-Rubaiee, R. Qiu, and D. Li, "Identifying Mubasher software products through sentiment analysis of Arabic tweets," in International Conference on Industrial Informatics and Computer Systems, Sharjah, United Arab Emirates, Mar. 2016, pp. 1–6. DOI: https://doi.org/10.1109/ICCSII.2016.7462396
S. Tartir and I. Abdul-Nabi, "Semantic Sentiment Analysis in Arabic Social Media," Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 2, pp. 229–233, Apr. 2017. DOI: https://doi.org/10.1016/j.jksuci.2016.11.011
S. Abuelenin, S. Elmougy, and E. Naguib, "Twitter Sentiment Analysis for Arabic Tweets," in International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt, Sep. 2017, pp. 467–476. DOI: https://doi.org/10.1007/978-3-319-64861-3_44
K. M. Alomari, H. M. ElSherif, and K. Shaalan, "Arabic Tweets Sentimental Analysis Using Machine Learning," in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Arras, France, Jun. 2017, pp. 602–610. DOI: https://doi.org/10.1007/978-3-319-60042-0_66
A. Mahmoud and T. Elghazaly, "Using Twitter to Monitor Political Sentiment for Arabic Slang," in Intelligent Natural Language Processing: Trends and Applications, K. Shaalan, A. E. Hassanien, and F. Tolba, Eds. New York, NY, USA: Springer, 2018, pp. 53–66. DOI: https://doi.org/10.1007/978-3-319-67056-0_4
R. Bouchlaghem, A. Elkhelifi, and R. Faiz, "SVM based approach for opinion classification in Arabic written tweets," in 12th International Conference of Computer Systems and Applications, Marrakech, Morocco, Nov. 2015, pp. 1–4. DOI: https://doi.org/10.1109/AICCSA.2015.7507153
L. S. Larkey, L. Ballesteros, and M. E. Connell, "Light Stemming for Arabic Information Retrieval," in Arabic Computational Morphology: Knowledge-based and Empirical Methods, A. Soudi, A. van den Bosch, and G. Neumann, Eds. Dordrecht, Netherlands: Springer, 2007, pp. 221–243. DOI: https://doi.org/10.1007/978-1-4020-6046-5_12
S. Khoja and R. Garside, Stemming Arabic Text. Bailrigg, England: Lancaster University, 1999.
A. Al-Thubaity, Q. Alqahtani, and A. Aljandal, "Sentiment lexicon for sentiment analysis of Saudi dialect tweets," Procedia Computer Science, vol. 142, pp. 301–307, Jan. 2018. DOI: https://doi.org/10.1016/j.procs.2018.10.494
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, pp. 1067–1073, Dec. 2016. DOI: https://doi.org/10.18517/ijaseit.6.6.1456
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, Dec. 2016. DOI: https://doi.org/10.1177/0165551515610513
A. Shoukry and A. Rafea, "A Hybrid Approach for Sentiment Classification of Egyptian Dialect Tweets," in First International Conference on Arabic Computational Linguistics, Cairo, Egypt, Apr. 2015, pp. 78–85. DOI: https://doi.org/10.1109/ACLing.2015.18
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, pp. 777–786, Sep. 2018. DOI: https://doi.org/10.13053/cys-22-3-3031
N. El-Naggar, Y. El-Sonbaty, and M. A. El-Nasr, "Sentiment analysis of modern standard Arabic and Egyptian dialectal Arabic tweets," in Computing Conference, London, UK, Jul. 2017, pp. 880–887. DOI: https://doi.org/10.1109/SAI.2017.8252198
R. Bouchlaghem, A. Elkhelifi, and R. Faiz, "Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features," in 17th International Conference on Intelligent Text Processing and Computational Linguistics, Konya, Turkey, Apr. 2016, pp. 320–334. DOI: https://doi.org/10.1007/978-3-319-75487-1_25
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, pp. 15–25, 2016.
L. Al-Horaibi and M. B. Khan, "Sentiment analysis of Arabic tweets using text mining techniques," in First International Workshop on Pattern Recognition, Tokyo, Japan, Dec. 2016, vol. 10011, pp. 288–292. DOI: https://doi.org/10.1117/12.2242187
R. Bouchlaghem, A. Elkhelifi, and R. Faiz, "A Machine Learning Approach For Classifying Sentiments in Arabic tweets," in 6th International Conference on Web Intelligence, Mining and Semantics, New York, NY, USA, Jun. 2016, pp. 1–6. DOI: https://doi.org/10.1145/2912845.2912874
Y. D. Setiyaningrum, A. F. Herdajanti, C. Supriyanto, and Muljono, "Classification of Twitter Contents using Chi-Square and K-Nearest Neighbour Algorithm," in International Seminar on Application for Technology of Information and Communication, Semarang, Indonesia, Sep. 2019, pp. 1–4. DOI: https://doi.org/10.1109/ISEMANTIC.2019.8884290
M. Alruily, "Towards Automatically Extracting Contextual Valence Shifters in Reviews of Saudi Universities," in 9th Symposium on Computer Applications & Industrial Electronics, Malaysia, Apr. 2019, pp. 278–281. DOI: https://doi.org/10.1109/ISCAIE.2019.8743859
N. Al-Twairesh and H. Al-Negheimish, "Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets," IEEE Access, vol. 7, pp. 84122–84131, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2924314
A. Alotaibi and M. H. Abul Hasanat, "Racism Detection in Twitter Using Deep Learning and Text Mining Techniques for the Arabic Language," in First International Conference of Smart Systems and Emerging Technologies, Riyadh, Saudi Arabia, Nov. 2020, pp. 161–164. DOI: https://doi.org/10.1109/SMART-TECH49988.2020.00047
A. H. Ombabi, W. Ouarda, and A. M. Alimi, "Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks," Social Network Analysis and Mining, vol. 10, no. 1, Jul. 2020, Art. no. 53. DOI: https://doi.org/10.1007/s13278-020-00668-1
R. Baly et al., "A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models," in Third Arabic Natural Language Processing Workshop, Valencia, Spain, Apr. 2017, pp. 110–118. DOI: https://doi.org/10.18653/v1/W17-1314
G. Badaro, R. Baly, H. Hajj, N. Habash, and W. El-Hajj, "A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining," in EMNLP 2014 Workshop on Arabic Natural Langauge Processing, Doha, Qatar, Oct. 2014, pp. 165–173. DOI: https://doi.org/10.3115/v1/W14-3623
N. Al-Twairesh, H. Al-Khalifa, A. Al-Salman, and Y. Al-Ohali, "AraSenTi-Tweet: A Corpus for Arabic Sentiment Analysis of Saudi Tweets," Procedia Computer Science, vol. 117, pp. 63–72, Jan. 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.094
S. M. Mohammad, M. Salameh, and S. Kiritchenko, "How Translation Alters Sentiment," Journal of Artificial Intelligence Research, vol. 55, pp. 95–130, Jan. 2016. DOI: https://doi.org/10.1613/jair.4787
R. M. Duwairi and I. Qarqaz, "Arabic Sentiment Analysis Using Supervised Classification," in International Conference on Future Internet of Things and Cloud, Barcelona, Spain, Aug. 2014, pp. 579–583. DOI: https://doi.org/10.1109/FiCloud.2014.100
R. M. Duwairi, "Sentiment analysis for dialectical Arabic," in 6th International Conference on Information and Communication Systems, Amman, Jordan, Apr. 2015, pp. 166–170. DOI: https://doi.org/10.1109/IACS.2015.7103221
H. S. Ibrahim, S. M. Abdou, and M. Gheith, "MIKA: A tagged corpus for modern standard Arabic and colloquial sentiment analysis," in 2nd International Conference on Recent Trends in Information Systems, Kolkata, India, Jul. 2015, pp. 353–358. DOI: https://doi.org/10.1109/ReTIS.2015.7232904
D. Mouheb, R. Albarghash, M. F. Mowakeh, Z. A. Aghbari, and I. Kamel, "Detection of Arabic Cyberbullying on Social Networks using Machine Learning," in 16th International Conference on Computer Systems and Applications, Abu Dhabi, United Arab Emirates, Nov. 2019, pp. 1–5. DOI: https://doi.org/10.1109/AICCSA47632.2019.9035276
A. Al-Laith and M. Shahbaz, "Tracking sentiment towards news entities from Arabic news on social media," Future Generation Computer Systems, vol. 118, pp. 467–484, May 2021. DOI: https://doi.org/10.1016/j.future.2021.01.015
A. Al-Thubaity, M. Alharbi, S. Alqahtani, and A. Aljandal, "A Saudi Dialect Twitter Corpus for Sentiment and Emotion Analysis," in 21st Saudi Computer Society National Computer Conference, Riyadh, Saudi Arabia, Apr. 2018, pp. 1–6. DOI: https://doi.org/10.1109/NCG.2018.8592998
F. Sadat, F. Mallek, M. Boudabous, R. Sellami, and A. Farzindar, "Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application – the case of Tunisian Arabic and the Social Media," in Workshop on Lexical and Grammatical Resources for Language Processing, Dublin, Ireland, Aug. 2014, pp. 102–110. DOI: https://doi.org/10.3115/v1/W14-5813
N. El-Makky et al., Sentiment Analysis of Colloquial Arabic Tweets. Alexandria, Egypt: Alexandria University, 2014.
M. F. Alhamid, S. Alsahli, M. Rawashdeh, and M. Alrashoud, "Detection and Visualization of Arabic Emotions on Social Emotion Map," in International Symposium on Multimedia, Taichung, Taiwan, Dec. 2017, pp. 378–381. DOI: https://doi.org/10.1109/ISM.2017.76
W. M. S. Yafooz, E. A. Hizam, and W. A. Alromema, "Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6845–6848, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4026
M. Mahyoob, J. Algaraady, M. Alrahiali, and A. Alblwi, "Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8525–8531, Jun. 2022. DOI: https://doi.org/10.48084/etasr.4865
I. A. Kandhro, S. Z. Jumani, F. Ali, Z. U. Shaikh, M. A. Arain, and A. A. Shaikh, "Performance Analysis of Hyperparameters on a Sentiment Analysis Model," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6016–6020, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3549
Downloads
How to Cite
License
Copyright (c) 2023 Tahani Alqurashi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
