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Public perception of the Chinese president’s visit to Saudi Arabia and the China–Arab Summit: sentiment analysis of Arabic tweets

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Abstract

In recent years, China and Saudi Arabia have had frequent exchanges in the political and economic fields, and public opinion evaluation is an essential aspect of evaluating the two countries’ current relations. This paper analyzes tweets in Arabic discussing the Chinese president’s visit to Saudi Arabia and the summits that were held between China and several Arab nations during that visit. The analysis uses one of CAMeLBERT’s sentiment analysis models targeted toward dialectal Arabic and employs modified preprocessing steps to enhance the model’s performance. The study finds that the majority of the tweets are neutral, due to extensive media coverage of the visit, and that positive tweets significantly outweigh negative tweets, which reflects that these events are perceived positively by the Arab public. Further content analysis reveals that the positive tweets discuss topics related to the outcomes and potentials for cooperation and strategic partnership between China and the Arab nations. Despite being outweighed by neutral and positive tweets, the negative tweets provide insight into the challenges that might face this partnership in future. These negative tweets discuss a variety of themes. First, they criticize America and Iran’s strategies and highlight these countries’ responses to the visit; second, they show concern over the potential subservience to China; third, they express fear that China might put its interests over others. The findings of this study are fundamental for the development of China–Arab relations as they provide crucial information about the challenges that need to be addressed in the future.

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References

  • Abdul Ghaffar MM (2018) Strategic development of Sino-GCC Relations: visions of Arabian gulf economic development and the belt and road initiative. Asian Jf Middle Eastern Islamic Studies 12(4):517–532

    Article  Google Scholar 

  • Abdul-Mageed, M., Elmadany, A. R., and Nagoudi, E. M. B., (2020a). ARBERT and MARBERT: Deep bidirectional transformers for Arabic.

  • Alghamdi N, Assiri F (2020) A comparison of fasttext implementations using Arabic text classification. In: Intelligent systems and applications: proceedings of the 2019 intelligent systems conference (IntelliSys), vol 2. Springer International Publishing

  • Alhumoud S (2020) Arabic sentiment analysis using deep learning for COVID-19 twitter data. Int J Comput Sci Netw Secur 20(9):132–138

    Google Scholar 

  • Alomari KM, ElSherif HM, Shaalan K (2017) Arabic Tweets sentimental analysis using machine learning. In: Benferhat S, Tabia K, Ali M (eds) Advances in Artificial Intelligence: From Theory to Practice. Springer International Publishing, Cham, pp 602–610. https://doi.org/10.1007/978-3-319-60042-0_66

    Chapter  Google Scholar 

  • Alqurashi T (2023) Arabic sentiment analysis for twitter data: a systematic literature review. Eng Technol Appl Sci Res 13(2):10292–10300

    Article  MathSciNet  Google Scholar 

  • Alshalabi H, Tiun S, OmarAL-AswadiAlezabi NFNKA (2022) Arabic light-based stemmer using new rules. J King Saud Univ Comput Inf Sci 34(9):6635–6642. https://doi.org/10.1016/j.jksuci.2021.08.017

    Article  Google Scholar 

  • Al-Twairesh N, Al-Matham R, Madi N, Almugren N, Al-Aljmi A-H, Alshalan S et al (2018) SUAR: towards building a corpus for the Saudi dialect. Procedia Comput Sci 142:72–82

    Article  Google Scholar 

  • Antoun W, Baly F, Hajj H (2020) AraBERT: transformer-based model for arabic language understanding. In: LREC 2020 workshop language resources and evaluation conference 11--16 may 2020 (pp. 9).

  • Ayyoub MA, Essa SB, Alsmadi I (2015) Lexicon-based sentiment analysis of Arabic tweets. Int J Social Netw Mining 2(2):101. https://doi.org/10.1504/ijsnm.2015.072280

    Article  Google Scholar 

  • Benabdallah L (2018) China’s relations with Africa and the Arab World: shared trends, different priorities. South African Institute of International Affairs. http://www.jstor.org/stable/resrep25989

  • Comito C, Falcone D, Talia D (2017) A peak detection method to uncover events from social media. In: 2017 IEEE international conference on data science and advanced analytics (DSAA), IEEE, pp 459–467

  • Cui J, Wang Z, Ho S-B, Cambria E (2023) Survey on sentiment analysis: evolution of research methods and topics. Artif Intell Rev 56(8):8469–8510. https://doi.org/10.1007/s10462-022-10386-z

    Article  Google Scholar 

  • Devlin J, Chang MW, Lee K, Toutanova K, (2019) BERT: pretraining of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Volume 1 (Long and Short Papers), pp. 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics

  • Elmadany A, Mubarak H, Magdy W, (2018) ArSAS: an Arabic speech-act and sentiment corpus of tweets. In: Proceedings of the Eleventh international conference on language resources and evaluation (LREC 2018), Paris, France. European Language Resources Association (ELRA)

  • El-Makky N, Nagi K, El-Ebshihy A, Apady E, Hafez O, Mostafa S, Ibrahim S (2015) Sentiment analysis of colloquial ArabicTweets. In: The 3rd ASE international conference on social informatics (SocialInformatics 2014) – Conference Proceedings, MA, USA

  • Elsaka T, Afyouni I, Hashem I, Al Aghbari Z (2022) Spatio-temporal sentiment mining of covid-19 arabic social media. ISPRS Int J Geo Inf 11(9):476

    Article  Google Scholar 

  • Hoh A (2019) China’s belt and road initiative in Central Asia and the Middle East. Digest Middle East Studies. https://doi.org/10.1111/dome.12191

    Article  Google Scholar 

  • Inoue G, Alhafni B, Baimukan N, Bouamor H, Habash N (2021) The interplay of variant, size, and task type in Arabic Pre-trained language models. arXiv preprint. Retrieved from https://arxiv.org/abs/2103.06678v2

  • Jalal MN (2014) The China-Arab states cooperation forum: achievements, challenges and prospects. J Middle Eastern Islamic Studies (in Asia) 8(2):1–21. https://doi.org/10.1080/19370679.2014.12023244

    Article  Google Scholar 

  • Lan WW, Chen Y, Xu W, Ritter A (2020) An empirical study of pre-trained transformers for Arabic information extraction. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 4727–4734, Online. Association for Computational Linguistics

  • Liangxiang J (2020) China and middle east security issues: challenges, perceptions and positions. Istituto Affari Internazionali (IAI). http://www.jstor.org/stable/resrep26107

  • Matrane Y, Benabbou F, Sael N (2023) A systematic literature review of Arabic dialect sentiment analysis. J King Saud Univ Comput Inf Sci 35(6):101570. https://doi.org/10.1016/j.jksuci.2023.101570

    Article  Google Scholar 

  • Nabil M, Aly M, Atiya A, (2015) ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2515– 2519, Lisbon, Portugal. Association for Computational Linguistics

  • Obeid O, Zalmout N, Khalifa S, Taji D, Oudah M, Alhafni B, Inoue G, Eryani F, Erdmann A, Habash N (2020) CAMeL tools: an open source python toolkit for Arabic natural language processing. In: Proceedings of the Twelfth language resources and evaluation conference, pp 7022–7032. European Language Resources Association. Marseille, France

  • Oussous A, Lahcen AA, Belfkih S (2019) Impact of text pre-processing and ensemble learning on Arabic sentiment analysis. In: Proceedings of the 2nd international conference on networking, information systems and security NISS19 doi:https://doi.org/10.1145/3320326.3320399

  • Oussous A, Benjelloun FZ, Lahcen AA, Belfkih S (2020) ASA: a framework for Arabic sentiment analysis. J Inf Sci 46(4):544–559

    Article  Google Scholar 

  • Rosenthal S, Farra N, Nakov P, (2017) Semeval-2017 task 4: sentiment analysis in twitter. In: Proceedings of the international workshop on semantic evaluation (SemEval), pp 501–516. Vancouver, Canada

  • Safaya A, Abdullatif M, Yuret D, (2020) KUISAIL at SemEval-2020 task 12: BERTCNN for offensive speech identification in social media. In: Proceedings of the Fourteenth workshop on semantic evaluation, pp 2054–2059, Barcelona (online). International Committee for Computational Linguistics

  • Sun D, Zoubir YH (2015) China’s economic diplomacy towards the Arab Countries: challenges ahead? J Contemp China 24(95):903–921. https://doi.org/10.1080/10670564.2015.1013379

    Article  Google Scholar 

  • Talafha B, Ali M, Za’ter ME, Seelawi H, Tuffaha I, Samir M, Farhan W, Al-Natsheh H, (2020) Multidialect Arabic BERT for country-level dialect identification. In: proceedings of the fifth arabic natural language processing workshop, pp 111–118, Barcelona, Spain (Online). Association for Computational Linguistics

  • Wang ZQ, Zhao J (2022) Internal structural changes in China-Arab League relations: characteristics, motivations and influences. Asian J Middle Eastern Islamic Studies 16(1):79–101. https://doi.org/10.1080/25765949.2022.2057067

    Article  Google Scholar 

  • Wazrah AAl, Alhumoud S (2021) Sentiment Analysis using stacked gated recurrent unit for Arabic Tweets. IEEE Access 9:137176–137187. https://doi.org/10.1109/ACCESS.2021.3114313

    Article  Google Scholar 

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Hassan, A.A.M. Public perception of the Chinese president’s visit to Saudi Arabia and the China–Arab Summit: sentiment analysis of Arabic tweets. Soc. Netw. Anal. Min. 14, 24 (2024). https://doi.org/10.1007/s13278-023-01174-w

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