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An Online Machine Learning Approach to Sentiment Analysis in Social Media

Author

Listed:
  • Jaber Alwidian
  • Tariq Khasawneh
  • Mahmoud Alsahlee
  • Ali Safia

Abstract

The online learning, is one that continuously adapts to arriving data, and gets updated incrementally instance by instance. In this paper, we compare the performance of different online machine learning algorithms for the task of sentiment analysis on challenging text datasets. We assess the models using a wide range of metrics, such as microF1, macroF1, accuracy, and running time. Our experiments have revealed that these online models provide a viable alternative to traditional offline machine learning in sentiment analysis, in fraction of the time.

Suggested Citation

  • Jaber Alwidian & Tariq Khasawneh & Mahmoud Alsahlee & Ali Safia, 2022. "An Online Machine Learning Approach to Sentiment Analysis in Social Media," Modern Applied Science, Canadian Center of Science and Education, vol. 16(4), pages 1-29, November.
    • Handle: RePEc:ibn:masjnl:v:16:y:2022:i:4:p:29
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    References listed on IDEAS

    as
    1. Jaber Alwidian & Sana Abdel Rahman & Maram Gnaim & Fatima Al-Taharwah, 2020. "Big Data Ingestion and Preparation Tools," Modern Applied Science, Canadian Center of Science and Education, vol. 14(9), pages 1-12, September.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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