skip to main content
10.1145/3333165.3333185acmotherconferencesArticle/Chapter ViewAbstractPublication PagesarabwicConference Proceedingsconference-collections
research-article

Deep Learning for Sentiment Analysis of Arabic Text

Published: 07 March 2019 Publication History

Abstract

Deep learning has been very successful in the past decades, especially in Computer Vision and Speech Recognition fields. It has been also used successfully in the Natural Language Processing field because of the availability of an enormous amount of online text data, such as social networks and reviews websites, which have gained a lot of popularity and success in the past years. Sentiment Analysis is one of the hottest applications of Natural Language Processing (NLP). Many researchers have done excellent work on Sentiment Analysis for English language. However, the amount of work on Sentiment Analysis for Arabic language is, in comparison, very limited due to the complexity of the Arabic language's morphology and orthography. Unlike the English language, Arabic has many different dialects which makes Sentiment Analysis for Arabic more difficult and challenging, especially when working on data collected from social networks, which is known to be unstructured and noisy. Most of the work that has been done on Sentiment Analysis of Arabic language, focused on using lexicons and basic machine learning models. In addition, most of the work has been done on small datasets because of the limited number of the available annotated datasets for Arabic language. This paper proposes state-of-the-art research for Sentiment Analysis of Arabic microblogging using new techniques, and a sophisticated Arabic text data preprocessing.

References

[1]
2018. INTERNET WORLD USERS BY LANGUAGE Top 10 Languages. (2018). https://www.internetworldstats.com/stats7.htm
[2]
Nawaf A Abdulla, Nizar A Ahmed, Mohammed A Shehab, and Mahmoud Al-Ayyoub. 2013. Arabic sentiment analysis: Lexicon-based and corpus-based. In Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on. IEEE, 1--6.
[3]
A Aziz Altowayan and Lixin Tao. 2016. Word embeddings for Arabic sentiment analysis. In Big Data (Big Data), 2016 IEEE International Conference on. IEEE, 3820--3825.
[4]
Mohamed Aly and Amir Atiya. 2013. Labr: A large scale arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2. 494--498.
[5]
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, Aug (2011), 2493--2537.
[6]
Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297.
[7]
Abdelghani Dahou, Shengwu Xiong, Junwei Zhou, Mohamed Houcine Haddoud, and Pengfei Duan. 2016. Word embeddings and convolutional neural network for arabic sentiment classification. In Proceedings of coling 2016, the 26th international conference on computational linguistics: Technical papers. 2418--2427.
[8]
Rehab M Duwairi. 2015. Sentiment analysis for dialectical Arabic. In 2015 6th International Conference on Information and Communication Systems (ICICS). IEEE, 166--170.
[9]
David A Ferrucci. 2012. Introduction to âĂIJthis is watsonâĂİ. IBM Journal of Research and Development 56, 3.4 (2012), 1--1.
[10]
Nizar Y Habash. 2010. Introduction to Arabic natural language processing. Synthesis Lectures on Human Language Technologies 3, 1 (2010), 1--187.
[11]
Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).
[12]
Ahmed Mourad and Kareem Darwish. 2013. Subjectivity and sentiment analysis of modern standard Arabic and Arabic microblogs. In Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis. 55--64.
[13]
Mahmoud Nabil, Mohamed Aly, and Amir Atiya. 2015. Astd: Arabic sentiment tweets dataset. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2515--2519.
[14]
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 79--86.
[15]
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 Task 4: Sentiment Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval '17). Association for Computational Linguistics, Vancouver, Canada.
[16]
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 task 4: Sentiment analysis in Twitter. In Proceedings of the 11th Inter-national Workshop on Semantic Evaluation (SemEval-2017). 502--518.
[17]
Janan Ben Salamah and Aymen Elkhlifi. 2014. Microblogging opinion mining approach for kuwaiti dialect. In The International Conference on Computing Technology and Information Management (ICCTIM2014). The Society of Digital Information and Wireless Communication, 388--396.
[18]
Amira Shoukry and Ahmed Rafea. 2015. A hybrid approach for sentiment classification of Egyptian Dialect Tweets. In Arabic Computational Linguistics (ACLing), 2015 First International Conference on. IEEE, 78--85.
[19]
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Advances in neural information processing systems. 649--657.
[20]
Ye Zhang and Byron Wallace. 2015. A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820 (2015).

Cited By

View all
  • (2025)A comprehensive survey on Arabic text augmentation: approaches, challenges, and applicationsNeural Computing and Applications10.1007/s00521-025-11020-zOnline publication date: 7-Feb-2025
  • (2024)Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweetsJournal of Cloud Computing10.1186/s13677-024-00644-613:1Online publication date: 9-Apr-2024
  • (2024)Impact of Machine Learning Integration in Qur’anic StudiesMachine Learning Research10.11648/j.mlr.20240902.149:2(54-63)Online publication date: 29-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ArabWIC 2019: Proceedings of the ArabWIC 6th Annual International Conference Research Track
March 2019
136 pages
ISBN:9781450360890
DOI:10.1145/3333165
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Google Inc.
  • Microsoft: Microsoft
  • Facebook: Facebook
  • ORACLE: ORACLE
  • IBM: IBM

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2019

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ArabWIC 2019

Acceptance Rates

ArabWIC 2019 Paper Acceptance Rate 20 of 36 submissions, 56%;
Overall Acceptance Rate 20 of 36 submissions, 56%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)18
  • Downloads (Last 6 weeks)1
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)A comprehensive survey on Arabic text augmentation: approaches, challenges, and applicationsNeural Computing and Applications10.1007/s00521-025-11020-zOnline publication date: 7-Feb-2025
  • (2024)Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweetsJournal of Cloud Computing10.1186/s13677-024-00644-613:1Online publication date: 9-Apr-2024
  • (2024)Impact of Machine Learning Integration in Qur’anic StudiesMachine Learning Research10.11648/j.mlr.20240902.149:2(54-63)Online publication date: 29-Oct-2024
  • (2024)Challenges and Approaches in Arabic Sentiment Analysis: A ReviewData Science and Communication10.1007/978-981-99-5435-3_36(499-519)Online publication date: 3-Jan-2024
  • (2023)Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approachData & Knowledge Engineering10.1016/j.datak.2022.102106143(102106)Online publication date: Jan-2023
  • (2022)Similarities between Arabic dialectsInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10277059:1Online publication date: 9-Apr-2022
  • (2022)Natural Language Processing Based Approach to Overcome Arabizi and Code Switching in Social Media Moroccan DialectAdvances in Information, Communication and Cybersecurity10.1007/978-3-030-91738-8_6(57-66)Online publication date: 1-Jan-2022
  • (2021)A Comparative Analysis of Arabic Text SteganographyApplied Sciences10.3390/app1115685111:15(6851)Online publication date: 26-Jul-2021
  • (2021)Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social mediaJournal of Big Data10.1186/s40537-021-00488-w8:1Online publication date: 2-Jul-2021
  • (2021)Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text PreprocessingComputational Intelligence and Neuroscience10.1155/2021/55387912021Online publication date: 1-Jan-2021
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media