Abstract
Social networks are considered today as the most popular interactive media where people can communicate, share information and express opinions without any limitation. The interest of the scientific community towards social contents has increased due to their importance in various fields such as marketing, sociology and politics. Several research areas related to social networks have emerged namely, community detection, sentiment analysis and topic detection. In this paper, we propose a domain-level topic detection approach for improving sentiment analysis in Arabic social content. The proposed approach is based on a supervised learning technique on Arabic collected data. Training dataset is mainly composed of Arabic press articles, while the test dataset is represented by posts and comments extracted from Arabic Facebook pages. Experimental evaluation showed that the proposed approach achieves good performances with precision values between 75.36% and 97.89%.
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
References
Mataoui, M., Zelmati, O., Boumechache, M.: A proposed lexicon-based sentiment analysis approach for the vernacular algerian arabic. Res. Comput. Sci. 110, 55–70 (2016)
Sebastiani, F.: Text categorization text mining and its applications. In: Text Mining and its Applications, A, pp. 109–129 (2005)
Zhu, X., Huang, J., Zhou, Z., Han, Y.: Chinese article classification oriented to social network based on convolutional neural networks. In: IEEE International Conference on Data Science in Cyberspace (DSC), pp. 33–36 (2016)
Ahmed, M.S., Khan, L., Rajeswari, M.: Using correlation based subspace clustering for multi-label text data classification. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 296–303 (2010)
Bairagi, V., Tapaswi, N.: Social network comment classification using fuzzy based classifier technique. In: Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1–7 (2016)
Faqeeh, M., Abdulla, N., Al-Ayyoub, M., Jararweh, Y., Quwaider, M.: Cross-lingual short-text document classification for facebook comments. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 573–578 (2014)
Ezzat, H., Ezzat, S., El-Beltagy, S., Ghanem, M.: Topicanalyzer: a system for unsupervised multi-label arabic topic categorization. In: 2012 International Conference on Innovations in Information Technology (IIT), pp. 220–225 (2012)
Gutierrez-Batista, K., Campaña, J.R., Martinez-Folgoso, S., Vila, M.A., Martin-Bautista, M.J.: About the effects of sentiments on topic detection in social networks. Int. J. Des. Nat. Ecodyn. 11, 387–395 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kaddouri, B., Mataoui, M. (2019). Domain-Level Topic Detection Approach for Improving Sentiment Analysis in Arabic Content. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-98352-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98351-6
Online ISBN: 978-3-319-98352-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)