Abstract
Sentiment analysis can be done at different levels of granularity: document, sentence, and aspect. In our case, we are interested in the aspect which presents the finest level of granularity. This level is named Aspect Based Sentiment Analysis. In fact, Aspect Based Sentiment Analysis (ABSA) requires two primordial steps: (i) extract entity aspects and (ii) determine the sentiments from all the aspects. Aspect extraction is an important step for the ABSA. It aims at detecting all the existing aspects in a sentence. The extraction of these aspects is complicated considering the presence of several challenges especially when the Aspect extraction is done in the Arabic language. In this paper, we propose a supervised system ADAL for aspects detection in the Arabic language. The obtained results indicate that our proposed method outperforms previous works to achieve 96% in terms of f-measure when applied to the same dataset provided by The International Workshop on Semantic Evaluation 2016 (SemEval-2016).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Top ten internet languages homepage.https://www.internetworldstats.com/stats7.htm.
- 2.
Madamira homepage, https://camel.abudhabi.nyu.edu/madamira/.
- 3.
Weka homepage, https://www.cs.waikato.ac.nz/ml/weka/.
- 4.
References
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O.,: Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jimenez-Zafra S., ¸ Eryigit, G.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval 2016. Association for Computational Linguistics, San Diego (2016)
AL-Smadi, M., Qawasmeh, O., Talafha, B., Quwaider, M.: Human annotated arabic dataset of book reviews for aspect based sentiment analysis. In: 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp, 726–730. IEEE (2015)
Naveen Kumar, L., Suresh Kumar, S.: Aspect based sentiment analysis survey. IOSR J. Comput. Eng. 18(2), 24–28 (2016). e-ISSN: 2278-0661, p-ISSN: 2278-8727
Hai, Z., Chang, K., Kim, J., Yang, C.: Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans. Knowl. Data Eng. 26(3), 623–634 (2014)
Piryani, R., Gupta, V., Singh, V.K., Ghose, U.: A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In: Advances in Computer and Computational Science (2017)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp, 168–177. ACM (2004)
Zhiqiang, T., Wenting, W.: DLIREC: aspect term extraction and term polarity classification system. In: SemEval, pp, 235–240 (2014)
Brun,C., Perez,J., Roux,C.: XRCE at SemEval-2016 Task 5: feedbacked ensemble modelling on syntactico-semantic knowledge for aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), pp, 282–286 (2018)
Alawami, A.: Aspect Extraction for Sentiment Analysis in Arabic Dialect. University of Pittsburgh, Diss (2017)
Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y., Qawasmeh, O.: Enhancing aspect-based sentiment analysis of arabic hotels’ reviews using morphological, syntactic and semantic features. Inf. Process. Manag. 56(2), 308–319 (2018)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh International Conference on Machine Learning, pp, 121–129 (1994)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Trigui, S., Boujelben, I., Jamoussi, S., Ben Ayed, Y. (2021). ADAL System: Aspect Detection for Arabic Language. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-49336-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49335-6
Online ISBN: 978-3-030-49336-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)