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Intelligent Natural Language Processing: Trends and Applications pp 53–66Cite as

Using Twitter to Monitor Political Sentiment for Arabic Slang

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Twitter is one of the most famous applications of social networks that allow users to communicate with each other and share their opinions and feelings in all types of topics: economics, business, science, social, religion, and politics in a very short message of information called Tweets. Users are usually written using colloquial Arabic and include a lot of slang. In this Paper, we studied sentiment analysis of Arabic text retrieved from a twitter focus on presidential elections in Egypt 2012. We are using Naïve Bayes (NB) which is a machine learning algorithm, one time by using N-Gram (unigram and bigram) and another time by using feature selection. The main objective of this paper is to measure the accuracy of each method and determine which method is more accurate for Arabic text classification. The results show that unigram and information gain attribute selection achieves the highest accuracy and the lowest error rate.

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Author information

Authors and Affiliations

  1. Computer Science Department, Institute of Statistical Studies and Research (ISSR) Cairo University, Giza, Egypt

    Amal Mahmoud & Tarek Elghazaly

Authors
  1. Amal Mahmoud
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  2. Tarek Elghazaly
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Corresponding author

Correspondence to Amal Mahmoud .

Editor information

Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Mahmoud, A., Elghazaly, T. (2018). Using Twitter to Monitor Political Sentiment for Arabic Slang. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-67056-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67055-3

  • Online ISBN: 978-3-319-67056-0

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