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Semantic Partitioning and Machine Learning in Sentiment Analysis

Author

Listed:
  • Ebaa Fayyoumi

    (Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Sahar Idwan

    (Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

Abstract

This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.

Suggested Citation

  • Ebaa Fayyoumi & Sahar Idwan, 2021. "Semantic Partitioning and Machine Learning in Sentiment Analysis," Data, MDPI, vol. 6(6), pages 1-17, June.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:6:p:67-:d:579190
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    References listed on IDEAS

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    1. Arnout B. Boot & Erik Tjong Kim Sang & Katinka Dijkstra & Rolf A. Zwaan, 2019. "How character limit affects language usage in tweets," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-13, December.
    2. Mohammed Rushdi-Saleh & M. Teresa Martín-Valdivia & L. Alfonso Ureña-López & José M. Perea-Ortega, 2011. "OCA: Opinion corpus for Arabic," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 2045-2054, October.
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    Cited by:

    1. Nattawat Khamphakdee & Pusadee Seresangtakul, 2023. "An Efficient Deep Learning for Thai Sentiment Analysis," Data, MDPI, vol. 8(5), pages 1-22, May.

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