IMPROVED DEEP LEARNING SENTIMENT ANALYSIS FOR ARABIC

Ahmed Binmahfoudh

Research output: Contribution to journalArticlepeer-review

Abstract

Sentiment Analysis (SA) has recently gained great interest in Natural Language Processing (NLP). In fact, NLP consists in extracting data from texts and categorizing certain tweets as Positive, Negative, or Neutral. In this paper, we also present our participation in the Arabic Sentiment Analysis Challenge organized by King Abdullah University of Science and Technology (KAUST). Data of interest are tweets written in Arabic language, which becomes more challengeable. In this manuscript, we present the introduced system and the bi-LSTM model. Also, detail the less efficient explored solutions. Our main objective is to extract the crucial semantic data in Arabic tweets. The obtained findings about Arabic twitter corpus reveal that the performance of the developed technique is better than that proposed in the literature. Official test accuracy scores are 0.7605 with Macro-F1 score.
Original languageEnglish (US)
Pages (from-to)1251-1260
Number of pages10
JournalJournal of Theoretical and Applied Information Technology
Volume101
Issue number3
StatePublished - Feb 15 2023
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2023-05-24
Acknowledgements: The author is grateful to help from Dr. Seifeddine Mechti (University of Sfax) as one of the team. Also, to King Abdullah University of Science and Technology (KAUST) in performing this competition and providing the data.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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