International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Survey of Arabic Thematic Sentiment Analysis Based on Topic Modeling

  • Basabain, Seham (Information Systems Faculty of Computing King AbdulAziz University)
  • Received : 2021.09.05
  • Published : 2021.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

Keywords

References

  1. Z. Mottaghinia, M.-R. Feizi-Derakhshi, L. Farzinvash, and P. Salehpour, 'A review of approaches for topic detection in Twitter', J. Exp. Theor. Artif. Intell., pp. 1-27, Jun. 2020, doi: 10.1080/0952813X.2020.1785019.
  2. A. Alharbi, M. Taileb, and M. Kalkatawi, 'Deep learning in Arabic sentiment analysis: An overview', J. Inf. Sci., vol. 47, no. 1, pp. 129-140, Feb. 2021, doi: 10.1177/0165551519865488.
  3. A. Hussain et al., 'Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations', medRxiv, 2020.
  4. X. Chen and H. Xie, 'A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature', Cogn. Comput., vol. 12, no. 6, pp. 1097-1129, Nov. 2020, doi: 10.1007/s12559-020-09745-1.
  5. M. Abdul-Mageed, M. Diab, and M. Korayem, 'Subjectivity and sentiment analysis of modern standard Arabic', in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, pp. 587-591.
  6. S. Basabain, 'Text-based Arabic Emotion Detection Challenges and Effective Approaches: A Review of the State-of-the-Art', Mar. 2021.
  7. A. Ghallab, A. Mohsen, and Y. Ali, 'Arabic Sentiment Analysis: A Systematic Literature Review', Appl. Comput. Intell. Soft Comput., vol. 2020, p. e7403128, Jan. 2020, doi: 10.1155/2020/7403128.
  8. S. M. Mohammad, Tony, and Yang, 'Tracking Sentiment in Mail: How Genders Differ on Emotional Axes', ArXiv13096347 Cs, Sep. 2013, Accessed: Feb. 03, 2021. [Online]. Available: http://arxiv.org/abs/1309.6347
  9. J. Li, Y. Rao, F. Jin, H. Chen, and X. Xiang, 'Multi-label maximum entropy model for social emotion classification over short text', Neurocomputing, vol. 210, pp. 247-256, Oct. 2016, doi: 10.1016/j.neucom.2016.03.088.
  10. T. Henriques, S. Silva, S. Bras, S. C. Soares, N. Almeida, and A. Teixeira, 'Emotionally-aware multimodal interfaces: Preliminary work on a generic affective modality', 2018, pp. 80-87.
  11. I. Abu Farha and W. Magdy, 'A comparative study of effective approaches for Arabic sentiment analysis', Inf. Process. Manag., vol. 58, no. 2, Art. no. 2, Mar. 2021, doi: 10.1016/j.ipm.2020.102438.
  12. K. M. Nahar, M. Ra'ed, M. Al-Shannaq, M. Daradkeh, and R. Malkawi, 'Direct text classifier for thematic arabic discourse documents.', Int Arab J Inf Technol, vol. 17, no. 3, pp. 394-403, 2020. https://doi.org/10.34028/iajit/17/3/13
  13. A. Alharbi, M. Taileb, and M. Kalkatawi, 'Deep learning in Arabic sentiment analysis: An overview', J. Inf. Sci., vol. 47, no. 1, Art. no. 1, Feb. 2021, doi: 10.1177/0165551519865488.
  14. A. Althagafi, G. Althobaiti, H. Alhakami, and T. Alsubait, 'Arabic Tweets Sentiment Analysis about Online Learning during COVID-19 in Saudi Arabia', Int. J. Adv. Comput. Sci. Appl., pp. 620-625, 2021.
  15. M. Gridach, H. Haddad, and H. Mulki, Empirical Evaluation of Word Representations on Arabic Sentiment Analysis. 2017.
  16. M. Bekkali, 'Arabic Sentiment Analysis using Different Representation Models', Int. J. Emerg. Trends Eng. Res., vol. 8, Aug. 2020, doi: 10.30534/ijeter/2020/79872020.
  17. R. Baly et al., 'OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model', in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, Canada, Aug. 2017, pp. 603-610. doi: 10.18653/v1/S17-2099.
  18. M. Alhawarat and M. Hegazi, 'Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents', IEEE Access, vol. 6, pp. 42740-42749, 2018, doi: 10.1109/ACCESS.2018.2852648.
  19. E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou, 'Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing', in Advanced Information Systems Engineering Workshops, Cham, 2020, pp. 95-108. doi: 10.1007/978-3-030-49165-9_9.
  20. G. Salton, A. Wong, and C.-S. Yang, 'A vector space model for automatic indexing', Commun. ACM, vol. 18, no. 11, pp. 613-620, 1975. https://doi.org/10.1145/361219.361220
  21. H. M. Alghamdi and A. Selamat, 'Topic detections in Arabic Dark websites using improved Vector Space Model', in 2012 4th Conference on Data Mining and Optimization (DMO), Sep. 2012, pp. 6-12. doi: 10.1109/DMO.2012.6329790.
  22. F. Bianchi, S. Terragni, D. Hovy, D. Nozza, and E. Fersini, 'Cross-lingual Contextualized Topic Models with Zero-shot Learning', ArXiv200407737 Cs, Feb. 2021, Accessed: Mar. 05, 2021. [Online]. Available: http://arxiv.org/abs/2004.07737
  23. J. Brandt et al., 'Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum', J. Comput. Soc. Sci., vol. 3, no. 1, pp. 167-188, Apr. 2020, doi: 10.1007/s42001-019-00061-9.
  24. R. Albalawi, T. H. Yeap, and M. Benyoucef, 'Using topic modeling methods for short-text data: A comparative analysis', Front. Artif. Intell., vol. 3, p. 42, 2020. https://doi.org/10.3389/frai.2020.00042
  25. B. Chen, L. Fan, and X. Fu, 'Sentiment Classification of Tourism Based on Rules and LDA Topic Model', in 2019 International Conference on Electronic Engineering and Informatics (EEI), 2019, pp. 471-475.
  26. F. Ding, X. Kang, S. Nishide, Z. Guan, and F. Ren, 'A fusion model for multi-label emotion classification based on BERT and topic clustering', in International Symposium on Artificial Intelligence and Robotics 2020, Oct. 2020, vol. 11574, p. 115740D. doi: 10.1117/12.2579255.
  27. A. Rafea and N. A. GabAllah, 'Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora', Procedia Comput. Sci., vol. 142, pp. 270-277, 2018, doi: 10.1016/j.procs.2018.10.492.
  28. A. E. Samy, S. R. El-Beltagy, and E. Hassanien, 'A Context Integrated Model for Multi-label Emotion Detection', in Arabic Computational Linguistics, vol. 142, K. Shaalan and S. R. ElBeltagy, Eds. Amsterdam: Elsevier Science Bv, 2018, pp. 61-71. doi: 10.1016/j.procs.2018.10.461.
  29. A. R. Alharbi, M. Hijji, and A. Aljaedi, 'Enhancing topic clustering for Arabic security news based on k-means and topic modelling', IET Netw., 2021.
  30. N. Alsaedi, P. Burnap, and O. Rana, 'Sensing real-world events using Arabic Twitter posts', 2016.
  31. W. Shafqat and Y.-C. Byun, 'A recommendation mechanism for under-emphasized tourist spots using topic modeling and sentiment analysis', Sustainability, vol. 12, no. 1, p. 320, 2020. https://doi.org/10.3390/su12010320
  32. I. AlAgha, 'Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives', J. Inf. Sci. Theory Pract., vol. 9, no. 1, pp. 35-53, 2021. https://doi.org/10.1633/JISTAP.2021.9.1.3
  33. M. Abdulaziz, M. Alsolamy, A. Alotaibi, and A. Alabbas, 'Topic based Sentiment Analysis for COVID-19 Tweets'.
  34. S. Wrycza and J. Maslankowski, 'Social media users' opinions on remote work during the COVID-19 pandemic. Thematic and sentiment analysis', Inf. Syst. Manag., vol. 37, no. 4, pp. 288-297, 2020. https://doi.org/10.1080/10580530.2020.1820631
  35. H. Yin, S. Yang, and J. Li, 'Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media', in International Conference on Advanced Data Mining and Applications, 2020, pp. 610-623.
  36. M. Daoud and N. A. Daoud, 'Sentimental event detection from Arabic tweets', Int. J. Bus. Intell. Data Min., vol. 17, no. 4, p. 471, 2020, doi: 10.1504/IJBIDM.2020.110378.
  37. M. Bekkali and A. Lachkar, 'Arabic Sentiment Analysis based on Topic Modeling', in Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society, Kenitra Morocco, Mar. 2019, pp. 1-6. doi: 10.1145/3314074.3314091.
  38. N. Habbat, H. Anoun, and L. Hassouni, 'Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets', Innov. Smart Cities Appl. Vol. 4, vol. 183, p. 147, 2021.
  39. S. Loria, 'textblob Documentation', Release 015, vol. 2, 2018.
  40. A. R. Alharbi, S. D. Alharbi, A. Aljaedi, and O. Akanbi, 'Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications', in 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Sep. 2020, pp. 1-6. doi: 10.1109/IICAIET49801.2020.9257842.
  41. T. Zarra, R. Chiheb, R. Moumen, R. Faizi, and A. E. Afia, 'Topic and sentiment model applied to the colloquial Arabic: a case study of Maghrebi Arabic', in Proceedings of the 2017 International Conference on Smart Digital Environment, Rabat Morocco, Jul. 2017, pp. 174-181. doi: 10.1145/3128128.3128155.
  42. A. R. Alharbi, S. D. Alharbi, A. Aljaedi, and O. Akanbi, 'Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications', in 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Sep. 2020, pp. 1-6. doi: 10.1109/IICAIET49801.2020.9257842.