Abstract
In this emerging age of social media, social networks become growing resources of user-generated material on the internet. These types of information resources, which are an expansive platform of humans’ emotions, opinions, feedback, and reviews, are considered powerful informants for big industries, markets, news, and many more. The great importance of these platforms, in conjunction with the increasingly high number of users generating contents in Arabic language, makes maiming the Arabic reviews in social networks necessary. This paper applies four automatic classification techniques; these techniques are Support vector Machine (SVM) and Back-Propagation Neural Networks (BPNN), Naïve Bayes, and Decision Tree. The main goal of this paper is to find a lightweight sentiment analysis approach for social networks’ reviews written in Arabic language. Results show that the SVM classifier achieves the highest accuracy rate, with 96.06% compared with other classifiers.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Nasukawa T., Jeonghee, Y.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture. ACM (2003)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web. ACM (2003)
Usage of content languages for websites. http://w3techs.com/technologies/overview/content_language/all
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Goh, A.T.C.: Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering 9(3), 143–151 (1995)
Chen, J., Huang, H., Tian, S., Qu, Y.: Feature selection for text classification with naïve bayes. Expert Systems with Applications 36, 5432–5435 (2009)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Santidhanyaroj, P., Khan, T., Gelowitz, C.M., Benedicenti, L.: A sentiment analysis prototype system for social network data, pp. 1–5 (2014)
Khasawneh, R.T., Wahsheh, H.A., AI-Kabi, M.N., Aismadi, I.M.: Sentiment Analysis of Arabic Social Media Content: A Comparative Study, pp. 101–106 (2013)
Kamal, A., Abulaish, M.: SMIEEE.: Statistical Features Identification for Sentiment Analysis using Machine Learning Techniques, pp. 178–181 (2013)
Abdul-Mageed, M., Diab, M.: AWATIF: a multi-genre corpus for modern standard arabic subjectivity and sentiment analysis. In: Proceedings of LREC, Istanbul, Turkey (2012)
Elhawary, M., Elfeky, M.: Mining arabic business reviews. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1108–1113. IEEE (2010)
El-Halees, A.: Arabic opinion mining using combined classification approach. In: Proceedings of the International Arab Conference on Information Technology, ACIT (2011)
Rushdi-Saleh, M., Martín-Valdivia, M., Ureña-López, L., Perea-Ortega, J.M.: Bilingual Experiments with an Arabic-English Corpus for Opinion Mining (2011)
Al-Subaihin, A., Al-Khalifa, H., Al-Salman, A-M.: A proposed sentiment analysis tool for modern arabic using human-based computing. In: Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services. ACM (2011)
Rijsbergen, C.J.V.: Information Retrieval: Butterworth-Heinemann (1979)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hammad, M., Al-awadi, M. (2016). Sentiment Analysis for Arabic Reviews in Social Networks Using Machine Learning. In: Latifi, S. (eds) Information Technology: New Generations. Advances in Intelligent Systems and Computing, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-319-32467-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-32467-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32466-1
Online ISBN: 978-3-319-32467-8
eBook Packages: EngineeringEngineering (R0)