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International Conference on Advanced Intelligent Systems and Informatics

AISI 2017: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 pp 467–476Cite as

Twitter Sentiment Analysis for Arabic Tweets

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

Abstract

Recently, increasing attention has been attracted to social networking sentiment analysis. Twitter is an online real-time social network and microblogging service that allows certified participants to distribute short posts called tweets. Twitter plays a major role in showing how consumers discover, research, and share information about brands and products. Sentiment analysis can be considered as a basic classification problem between three classes (Positive, Negative, and Neutral). Much work had been done on sentiment analysis in English while less work had been done on other languages like Arabic. Social media and blogs used by individuals are typically in Dialect Arabic. This work is focused on exploring efficient ways to increase the accuracy of sentiment analysis in Egyptian Arabic. The proposed system is based on semantic orientation (Cosine similarity and ISRI Arabic stemmer) and machine learning techniques. Experimental results showed that it achieves an overall accuracy of 92.98% using Linear Support Vector Machine.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

    Sherihan Abuelenin, Samir Elmougy & Eman Naguib

Authors
  1. Sherihan Abuelenin
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  2. Samir Elmougy
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  3. Eman Naguib
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Corresponding author

Correspondence to Eman Naguib .

Editor information

Editors and Affiliations

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

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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© 2018 Springer International Publishing AG

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Abuelenin, S., Elmougy, S., Naguib, E. (2018). Twitter Sentiment Analysis for Arabic Tweets. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

  • eBook Packages: EngineeringEngineering (R0)

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