More Web Proxy on the site http://driver.im/

research-article

Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews

Authors:

Ibrahim Al-Jarrah,

Ahmad M. Mustafa,

Hassan NajadatAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 7

Article No.: 200, Pages 1 - 18

https://doi.org/10.1145/3605146

Published: 20 July 2023 Publication History

Abstract

Business customers and consumers share their reviews online on social platforms such as Twitter. Therefore, Twitter data sentiment analysis is extremely useful for both research and commercial purposes. Manually analyzing reviews takes a long time and effort, hence, automatic sentiment analysis is required. In this article, we address aspect-based sentiment analysis for Arabic food delivery reviews using several deep learning approaches. In particular, we propose to use Transformer-based models (GigaBERT and AraBERT), Bi-LSTM-CRF, and LSTM, as well as a classical machine learning algorithm (SVM). We also present our dataset of food delivery service reviews, which we collected from Twitter. We annotated them and used them for training and evaluating our approaches.

The experiments show that both GigaBERT and AraBERT outperformed the other models in all the tasks. The Transformer-based models received F1-scores of 77% in the aspect terms detection task, 82% in the Aspect category detection task, and 81% in the aspect polarity detection task, gaining 2%, 4%, and 4% over Bi-LSTM-CRF and LSTM in the first, second, and third tasks, respectively.

References

[1]

Wissam Antoun, Fady Baly, and Hazem Hajj. [n. d.]. AraBERT: Transformer-based model for Arabic language understanding. In LREC 2020 Workshop Language Resources and Evaluation Conference 11–16 May 2020. 9.

[2]

Erik F. Sang and Jorn Veenstra. 1999. Representing text chunks. arXiv preprint cs/9907006 (1999).

[3]

Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. 2005. Bidirectional LSTM networks for improved phoneme classification and recognition. In International Conference on Artificial Neural Networks. Springer, 799–804.

Digital Library

[4]

A. Phillip Owens 3rd and Nigel Mackman. 2011. MP’s and VTE’s: Fact or fiction. Thrombosis Research 128, 6 (2011), 505–506.

[5]

Dedi Rianto Rahadi and Leon Andretti Abdillah. 2013. The utilization of social networking as promotion media (Case study: Handicraft business in Palembang). arXiv preprint arXiv:1312.3532 (2013).

[6]

Gilbert Badaro, Ramy Baly, Hazem Hajj, Nizar Habash, and Wassim El-Hajj. 2014. A large scale Arabic sentiment lexicon for Arabic opinion mining. In Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP’14). 165–173.

[7]

Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams. 2014. Twitter. https://en.wikipedia.org/wiki/Twitter.

[8]

Mohammad Al-Smadi, Omar Qawasmeh, Bashar Talafha, and Muhannad Quwaider. 2015. Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In 2015 3rd International Conference on Future Internet of Things and Cloud. IEEE, 726–730.

Digital Library

[9]

Maria Pontiki, Dimitrios Galanis, Harris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015. SemEval-2015 Task 12: Aspect based sentiment analysis. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15). 486–495.

[10]

Kumar Ravi and Vadlamani Ravi. 2015. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-based Systems 89 (2015), 14–46.

Digital Library

[11]

Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Huda Al-Sarhan, and Yaser Jararweh. 2016. An aspect-based sentiment analysis approach to evaluating Arabic news affect on readers. J. Univers. Comput. Sci. 22, 5 (2016), 630–649.

[12]

AL-Smadi Mohammad, Omar Qwasmeh, Bashar Talafha, Mahmoud Al-Ayyoub, Yaser Jararweh, and Elhadj Benkhelifa. 2016. An enhanced framework for aspect-based sentiment analysis of Hotels’ reviews: Arabic reviews case study. In 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST’16). IEEE, 98–103.

[13]

Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016. Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv:1603.06679 (2016).

[14]

M. S. Mubarok, Adiwijaya, and M. D. Aldhi. 2017. Aspect-based sentiment analysis to review products using naïve Bayes. In AIP Conference Proceedings. 020060.

[15]

Rajesh Piryani, D. Madhavi, and Vivek Kumar Singh. 2017. Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Information Processing & Management 53, 1 (2017), 122–150.

[16]

Ahmad Al-Sallab, Ramy Baly, Hazem Hajj, Khaled Bashir Shaban, Wassim El-Hajj, and Gilbert Badaro. 2017. Aroma: A recursive deep learning model for opinion mining in Arabic as a low resource language. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 16, 4 (2017), 1–20.

Digital Library

[17]

Mahmoud Al-Ayyoub, Amal Gigieh, Areej Al-Qwaqenah, Mohammed N. Al-Kabi, Bashar Talafhah, and Izzat Alsmadi. 2017. Aspect-based sentiment analysis of Arabic laptop. In ACIT’2017, The International Arab Conference on Information Technology.

[18]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. CoRR abs/1706.03762 (2017). arXiv:1706.03762 http://arxiv.org/abs/1706.03762.

[19]

Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017. Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923 (2017).

[20]

Ike Pertiwi Windasari, Fajar Nurul Uzzi, and Kodrat Iman Satoto. 2017. Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. In 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE’17). IEEE, 266–269.

[21]

Rrubaa Panchendrarajan and Aravindh Amaresan. 2018. Bidirectional LSTM-CRF for named entity recognition. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation.

[22]

Sridhar Ramaswamy and Natalie DeClerck. 2018. Customer perception analysis using deep learning and NLP. Procedia Computer Science 140 (2018), 170–178.

[23]

Mohammad Al-Smadi, Omar Qawasmeh, Mahmoud Al-Ayyoub, Yaser Jararweh, and Brij Gupta. 2018. Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science 27 (2018), 386–393.

[24]

Jingcheng Du, Yaoyun Zhang, Jianhong Luo, Yuxi Jia, Qiang Wei, Cui Tao, and Hua Xu. 2018. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics and Decision Making 18, 2 (2018), 77–87.

[25]

Natalia Klyueva, Yunfei Long, Chu-Ren Huang, and Qin Lu. 2018. Food-related sentiment analysis for Cantonese. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing.

[26]

Mohammed Matuq Ashi, Muazzam Ahmed Siddiqui, and Farrukh Nadeem. 2018. Pre-trained word embeddings for Arabic aspect-based sentiment analysis of airline tweets. In International Conference on Advanced Intelligent Systems and Informatics. Springer, 241–251.

[27]

Zulkarnian Zulkarnain, Isti Surjandari, and Reggia Aldiana Wayasti. 2018. Sentiment analysis for mining customer opinion on Twitter: A case study of ride-hailing service provider. In 2018 5th International Conference on Information Science and Control Engineering (ICISCE’18). IEEE, 512–516.

[28]

Aitor García-Pablos, Montse Cuadros, and German Rigau. 2018. W2VLDA: Almost unsupervised system for aspect based sentiment analysis. Expert Systems with Applications 91 (2018), 127–137.

[29]

Sana Trigui, Ines Boujelben, Salma Jamoussi, and Yassine Ben Ayed. 2019. ADAL system: Aspect detection for Arabic language. In International Conference on Hybrid Intelligent Systems. Springer, 31–40.

[30]

Nilesh Korde, Gaurav Kawade, Sunita Rawat, Kavita Kalambe, and Abhijeet Thakare. 2019. Exploration of opinion from Twitter data. International Journal of Recent Technology and Engineering (IJRTE) 8 (2019).

[31]

Andia Enggar Mayasari and Anggit Dwi Hartanto. 2019. User satisfaction levels sentiment analysis toward goods delivery service on Twitter using support vector machine algorithm (SVM). In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE’19). IEEE, 239–243.

[32]

Mohammad Al-Smadi, Bashar Talafha, Mahmoud Al-Ayyoub, and Yaser Jararweh. 2019. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics 10, 8 (2019), 2163–2175.

[33]

Norah Fahad Alshammari and Amal Abdullah AlMansour. 2020. Aspect-based sentiment analysis for Arabic content in social media. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE’20). IEEE, 1–6.

[34]

Sufyan Areed, Omar Alqaryouti, Bilal Siyam, and Khaled Shaalan. 2020. Aspect-based sentiment analysis for Arabic government reviews. In Recent Advances in NLP: The case of Arabic language. Springer, 143–162.

[35]

Manar Alassaf and Ali Mustafa Qamar. 2020. Aspect-based sentiment analysis of Arabic tweets in the education sector using a hybrid feature selection method. In 2020 14th International Conference on Innovations in Information Technology (IIT’20). IEEE, 178–185.

[36]

Manjubala Bisi, Ankam Divija, Sowmya Namala, and Rohan Sarap. 2020. CNN-BPSO model for multi classification of tweets. In 2020 IEEE 4th Conference on Information & Communication Technology (CICT’20). IEEE, 1–5.

[37]

Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, and Richard Socher. 2020. Co-search: COVID-19 information retrieval with semantic search, question answering, and abstractive summarization. arXiv preprint arXiv:2006.09595 (2020).

[38]

Irfan Nasrullah and Rila Mandala. 2020. Comparison intent recognition on food delivery service complaint in Twitter with recurrent and convolutional neural network. IT for Society 5, 1 (2020).

[39]

Satyendra Praneel Reddy Karri and B. Santhosh Kumar. 2020. Deep learning techniques for implementation of chatbots. In 2020 International Conference on Computer Communication and Informatics (ICCCI’20). IEEE, 1–5.

[40]

Wuwei Lan, Yang Chen, Wei Xu, and Alan Ritter. 2020. An empirical study of pre-trained transformers for Arabic information extraction. In Proceedings of the 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP’20).

[41]

Wuwei Lan, Yang Chen, Wei Xu, and Alan Ritter. 2020. An empirical study of pre-trained transformers for Arabic information extraction. arXiv preprint arXiv:2004.14519 (2020).

[42]

Ghady Alhamad and Mohamad-Bassam Kurdy. 2020. Feature-based sentiment analysis for Arabic language. International Journal of Advanced Computer Science and Applications 11, 11 (2020).

[43]

Abdullah M. Bdeir and Farid Ibrahim. 2020. A framework for Arabic tweets multi-label classification using word embedding and neural networks algorithms. In Proceedings of the 2020 2nd International Conference on Big Data Engineering. 105–112.

Digital Library

[44]

Raja Masadeh and Bassam Hammo Sa’ad Al-Azzam. 2020. A hybrid approach of lexicon-based and corpus-based techniques for Arabic book aspect and review polarity detection. International Journal of Advanced Trends in Computer Science and Engineering 9 (2020).

[45]

Ravindra Kumar Singh and Harsh Kumar Verma. 2020. Influence of social media analytics on online food delivery systems. International Journal of Information System Modeling and Design (IJISMD) 11, 3 (2020), 1–21.

[46]

Maryam Heidari and Setareh Rafatirad. 2020. Using transfer learning approach to implement convolutional neural network model to recommend airline tickets by using online reviews. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization. 1–6. DOI:

[47]

Derek A. Pisner and David M. Schnyer. 2020. Support vector machine. In Machine Learning. Elsevier, 101–121.

[48]

Salima Behdenna, Fatiha Barigou, and Ghalem Belalem. 2020. Towards semantic aspect-based sentiment analysis for Arabic reviews. International Journal of Information Systems in the Service Sector (IJISSS) 12, 4 (2020), 1–13.

[49]

Youwei Song, Jiahai Wang, Zhiwei Liang, Zhiyue Liu, and Tao Jiang. 2020. Utilizing BERT intermediate layers for aspect based sentiment analysis and natural language inference. arXiv preprint arXiv:2002.04815 (2020).

[50]

Harshith Nadendla. 2020. Why are LSTMs struggling to matchup with transformers?https://medium.com/analytics-vidhya/why-are-lstms-struggling-to-matchup-with-transformers-a1cc5b2557e3.

[51]

Ruba Obiedat, Duha Al-Darras, Esra Alzaghoul, and Osama Harfoushi. 2021. Arabic aspect-based sentiment analysis: A systematic literature review. IEEE Access (2021).

[52]

Mohammed M. Abdelgwad, Taysir Hassan A. Soliman, Ahmed I. Taloba, and Mohamed Fawzy Farghaly. 2021. Arabic aspect based sentiment analysis using bidirectional GRU based models. Journal of King Saud University-Computer and Information Sciences (2021).

[53]

Ibrahim Abu Farha and Walid Magdy. 2021. Benchmarking transformer-based language models for Arabic sentiment and sarcasm detection. In Proceedings of the 6th Arabic Natural Language Processing Workshop. 21–31.

[54]

Saja Al-Dabet, Sara Tedmori, and AL-Smadi Mohammad. 2021. Enhancing Arabic aspect-based sentiment analysis using deep learning models. Computer Speech & Language 69 (2021), 101224.

[55]

Azadeh Mohammadi and Anis Shaverizade. 2021. Ensemble deep learning for aspect-based sentiment analysis. International Journal of Nonlinear Analysis and Applications 12, Special Issue (2021), 29–38.

[56]

Krutuja S. Lasne, Sejal S. Nandrekar, Ashraf A. Khan, and Tushar Ghorpade. 2021. Food reviews classification using multi-label convolutional neural network text classifier. In ITM Web of Conferences, Vol. 40. EDP Sciences, 01009.

[57]

Peng Liu, Lemei Zhang, and Jon Atle Gulla. 2021. Multilingual review-aware deep recommender system via aspect-based sentiment analysis. ACM Transactions on Information Systems (TOIS) 39, 2 (2021), 1–33.

Digital Library

[58]

Jahanur Biswas, Md Mahbubur Rahman, Al Amin Biswas, Md Akib Zabed Khan, Aditya Rajbongshi, and Hasnaine Amin Niloy. 2021. Sentiment analysis on user reaction for online food delivery services using BERT model. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS’21), Vol. 1. IEEE, 1019–1023.

[59]

Salima Behdenna, Barigou Fatiha, and Ghalem Belalem. 2022. Ontology-based approach to enhance explicit aspect extraction in standard Arabic reviews. International Journal of Computing and Digital Systems 11, 1 (2022), 277–287.

[60]

Rajae Bensoltane and Taher Zaki. 2022. Towards Arabic aspect-based sentiment analysis: A transfer learning-based approach. Social Network Analysis and Mining 12, 1 (2022), 1–16.

Cited By

Alotaibi R(2024)Aspect-based sentiment analysis of open-ended responses in student course evaluation surveysThermal Science10.2298/TSCI2406037A28:6 Part B(5037-5047)Online publication date: 2024
https://doi.org/10.2298/TSCI2406037A
Aljeezani OAlomari DAhmad I(2024)Arabic App Reviews: Analysis and ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3708987Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1145/3708987
Alshaikh KAlmatrafi OAbushark Y(2024)BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case StudyIEEE Access10.1109/ACCESS.2023.334834212(2288-2302)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3348342
Show More Cited By

Index Terms

Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

Recommendations

Identification of Conflict Opinion in Aspect-Based Sentiment Analysis using BERT-based Method
IC3INA '22: Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

Aspect-based sentiment analysis (ABSA) is an NLP task for predicting sentiment polarities of specific aspects in a given opinion sentence. Recent research shows that deep learning and language modeling like BERT has become state-of-the-art in NLP tasks,...
Hybrid sentiment classification on twitter aspect-based sentiment analysis

Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the ...
Sentence compression for aspect-based sentiment analysis

Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 7

July 2023

422 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3610376

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2023

Online AM: 18 June 2023

Accepted: 09 June 2023

Revised: 05 April 2023

Received: 28 June 2022

Published in TALLIP Volume 22, Issue 7

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
444
Total Downloads

Downloads (Last 12 months)197
Downloads (Last 6 weeks)8

Reflects downloads up to 28 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Alotaibi R(2024)Aspect-based sentiment analysis of open-ended responses in student course evaluation surveysThermal Science10.2298/TSCI2406037A28:6 Part B(5037-5047)Online publication date: 2024
https://doi.org/10.2298/TSCI2406037A
Aljeezani OAlomari DAhmad I(2024)Arabic App Reviews: Analysis and ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3708987Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1145/3708987
Alshaikh KAlmatrafi OAbushark Y(2024)BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case StudyIEEE Access10.1109/ACCESS.2023.334834212(2288-2302)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3348342
Alsalem AAbudalfa S(2024)Empirical Analysis for Arabic Target-Dependent Sentiment Classification Using LLMs2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)10.1109/3ict64318.2024.10824564(170-176)Online publication date: 17-Nov-2024
https://doi.org/10.1109/3ict64318.2024.10824564
Ambreen SIqbal MAsghar MMazhar TKhattak UKhan MHamam H(2024)Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic textSocial Network Analysis and Mining10.1007/s13278-024-01356-014:1Online publication date: 15-Oct-2024
https://doi.org/10.1007/s13278-024-01356-0
Német BRigó ASárdy M(2023)A bőrgyógyászati páciensek komplex pszichodermatológiai ellátásának szükségességeMentálhigiéné és Pszichoszomatika10.1556/0406.2023.0004424:4(307-317)Online publication date: 26-Dec-2023
https://doi.org/10.1556/0406.2023.00044
Kora RMohammed A(2023)A Comprehensive Review on Transformers Models For Text Classification2023 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC58832.2023.10278387(1-7)Online publication date: 27-Sep-2023
https://doi.org/10.1109/MIUCC58832.2023.10278387

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents