DOI QR코드

DOI QR Code

The Effect of the Sentence Location on Arabic Sentiment Analysis

  • Alotaibi, Saud S. (Umm Al-Qura University, Information Systems Department)
  • Received : 2022.05.05
  • Published : 2022.05.30

Abstract

Rich morphology language such as Arabic needs more investigation and method to improve the sentiment analysis task. Using all document parts in the process of the sentiment analysis may add some unnecessary information to the classifier. Therefore, this paper shows the ongoing work to use sentence location as a feature with Arabic sentiment analysis. Our proposed method employs a supervised sentiment classification method by enriching the feature space model with some information from the document. The experiments and evaluations that were conducted in this work show that our proposed feature in the sentiment analysis for Arabic improves the performance of the classifier compared to the baseline model.

Keywords

References

  1. Abdul-Mageed, M., Diab, M.T., Korayem, M.: Subjectivity and Sentiment Analysis of Modern Standard Arabic. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2. HLT '11, Stroudsburg, PA, USA, Association for Computational Linguistics (2011) 587-591
  2. Abdul-Mageed, M., Diab, M.: AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis. Proceedings of LREC, Istanbul, Turkey (2012) Pages 19-28
  3. Abdul-Mageed, M., Kubler, S., Diab, M.: SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media. WASSA 2012 (2012)
  4. El-Halees, A.: Arabic opinion mining using combined classification approach. In: Proceeding The International Arab Conference On Information Technology, Azrqa, Jordan. (2011)
  5. Abbasi, A., Chen, H., Salem, A.: Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums. ACM Trans. Inf. Syst. 26 (2008) 12:1-12:34
  6. Farra, N., Challita, E., Assi, R.A., Hajj, H.: Sentence-Level and Document-Level Sentiment Mining for Arabic Texts. In: Data Mining Workshops (ICDMW), 2010 IEEE International Conference on. (2010) 1114-1119
  7. El-Khair, I.A.: Effects of stop words elimination for arabic information retrieval: a comparative study. International Journal of Computing & Information Sciences 4 (2006) 119-133
  8. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011) 2825-2830.