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

Authors
  • Guellil, Imane1, 2
  • Azouaou, Faical2
  • Chiclana, Francisco3, 4
  • 1 Aston University and Folding Space, Birmingham, UK , Birmingham (United Kingdom)
  • 2 Ecole nationale Supérieure d’Informatique, Oued-Smar, Alger, 16309, Algeria , Oued-Smar (Algeria)
  • 3 De Montfort University, Leicester, UK , Leicester (United Kingdom)
  • 4 University of Granada, Granada, Spain , Granada (Spain)
Type
Published Article
Journal
Social Network Analysis and Mining
Publisher
Springer Vienna
Publication Date
Aug 31, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1007/s13278-020-00688-x
Source
Springer Nature
Keywords
License
Yellow

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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