Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32220
Performance Evaluation of an Ontology-Based Arabic Sentiment Analysis

Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem


Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic ABSA approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: Sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 259


[1] B. Liu, “Sentiment analysis and opinion mining,” Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1–167, 2012.
[2] S. Behdenna, F. Barigou, and G. Belalem, “Sentiment Analysis at Document Level,” in Conf. International Conference on Smart Trends for Information Technology and Computer Communications. Springer, Singapore, 2016, pp. 159-168.
[3] S. Behdenna, F. Barigou, and G. Belalem, “Sentiment Analysis of Arabic Tweets: Opinion Target Extraction,” Digital Information Management J., vol. 16, no. 6, pp. 325, 2018.
[4] S. Al-Dabet, S. Tedmori, and M. Al-Smadi, “Extracting Opinion Targets Using Attention-Based Neural Model,” SN Computer Science J., vol. 1, no 5, pp. 1-10, 2020.
[5] S. Areed, O. Alqaryouti, B. Siyam, and K. Shaalan, “Aspect-based sentiment analysis for Arabic government reviews” In Recent Advances in NLP: The Case of Arabic Language, 2020, pp. 143-162.
[6] M. Al-Smadi, O. Qawasmeh, B. Talafha, and M. Quwaider, “Human annotated arabic dataset of book reviews for aspect based sentiment analysis,” in Conf. 2015 3rd International Conference on Future Internet of Things and Cloud. IEEE, pp. 726-730, 2015.
[7] M. Al-Smadi, B. Talafha, M. Al-Ayyoub, and Y. Jararweh, “Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews,” International Journal of Machine Learning and Cybernetics., vol. 10, no 8, pp. 2163-2175, 2019.
[8] S. Behdenna, F. Barigou, and G. Belalem, “Towards Semantic Aspect Based Sentiment Analysis for Arabic Reviews.” International Journal of Information Systems in the Service Sector (IJISSS) J., vol. 12, no. 4, pp. 1-13, 2020.
[9] F.Baader, I. Horrocks, and U. Sattler, “Description logics as ontology languages for the semantic web,” in Conf. Mechanizing mathematical reasoning, Springer, Berlin,2005, pp. 228-248.
[10] G. Shu, O. F. Rana, N. J. Avis, and C. Dingfang, “Ontology-based semantic matchmaking approach,” Advances in engineering software J., vol. 38, no. 1, pp.59-67, 2007.
[11] M.Aly, A. Atiya, “Labr: A large scale arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 494-498, 2013.