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Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

Abstract

With the huge amount of daily generated social networks posts, reviews, ratings, recommendations and other forms of online expressions, the web 2.0 has turned into a crucial opinion rich resource. Since others’ opinions seem to be determinant when making a decision both on individual and organizational level, several researches are currently looking to the sentiment analysis.

In this paper, we deal with sentiment analysis in Arabic written Twitter posts. Our proposed approach is leveraging a rich set of multilevel features like syntactic, surface-form, tweet-specific and linguistically motivated features. Sentiment features are also applied, being mainly inferred from both novel general-purpose as well as tweet-specific sentiment lexicons for Arabic words.

Several supervised classification algorithms (Support Vector Machines, Naive Bayes, Decision tree and Random Forest) were applied on our data focusing on modern standard Arabic (MSA) tweets. The experimental results using the proposed resources and methods indicate high performance levels given the challenge imposed by the Arabic language particularities.

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Notes

  1. 1.

    http://semiocast.com/.

  2. 2.

    http://twitter4j.org/en/index.html.

  3. 3.

    http://nlp.stanford.edu/.

  4. 4.

    http://textometrie.ens-lyon.fr/?lang=en.

  5. 5.

    https://wordnet.princeton.edu.

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Author information

Authors and Affiliations

  1. LARODEC, ISG, University of Tunis, Tunis, Tunisia

    Rihab Bouchlaghem

  2. Paris Sorbonne University, Paris, France

    Aymen Elkhelifi

  3. LARODEC, IHEC, University of Carthage, Tunis, Tunisia

    Rim Faiz

Authors
  1. Rihab Bouchlaghem
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  2. Aymen Elkhelifi
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  3. Rim Faiz
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Corresponding author

Correspondence to Rihab Bouchlaghem .

Editor information

Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Bouchlaghem, R., Elkhelifi, A., Faiz, R. (2018). Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75486-4

  • Online ISBN: 978-3-319-75487-1

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