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ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

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Abstract

A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1 score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results, respectively, represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature.

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Notes

  1. More details related to deep learning algorithms are presented in the survey of Schmidhuber (2015).

  2. https://www.imdb.com/.

  3. A detailed survey presenting deep learning for sentiment analysis was presented by Zhang et al. (2018).

  4. https://en.glosbe.com/.

  5. https://www.mturk.com/.

  6. https://glosbe.com/en/arq/excellent.

  7. https://www.socialbakers.com/statistics/facebook/pages/total/.

  8. https://www.facebook.com/MustafaHosny/.

  9. https://www.facebook.com/ooredooqatar/

  10. https://www.facebook.com/EnnaharTv/.

  11. http://restfb.com/.

  12. www.echoroukonline.com/ara/.

  13. www.elkhabar.com.

  14. www.ennaharonline.com.

  15. https://radimrehurek.com/gensim/apiref.html.

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Authors and Affiliations

  1. Aston University and Folding Space, Birmingham, UK

    Imane Guellil

  2. Laboratoire des Méthodes de Conception des Systèmes, Ecole nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algeria

    Imane Guellil & Faical Azouaou

  3. Institute of Artificial Intelligence (IAI), Faculty of Computing, Engineering and Media, De Montfort University, Leicester, UK

    Francisco Chiclana

  4. Andalusian Research Institute on Data Science and Computational Intelligence, University of Granada, Granada, Spain

    Francisco Chiclana

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  1. Imane Guellil
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Guellil, I., Azouaou, F. & Chiclana, F. ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages. Soc. Netw. Anal. Min. 10, 75 (2020). https://doi.org/10.1007/s13278-020-00688-x

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