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.

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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
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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

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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

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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].

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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

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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

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