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Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Author

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  • Osama Harfoushi
  • Dana Hasan
  • Ruba Obiedat

Abstract

The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

Suggested Citation

  • Osama Harfoushi & Dana Hasan & Ruba Obiedat, 2018. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison," Modern Applied Science, Canadian Center of Science and Education, vol. 12(7), pages 1-49, July.
    • Handle: RePEc:ibn:masjnl:v:12:y:2018:i:7:p:49
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    Cited by:

    1. Schwab Bakombo & Paulette Ewalefo & Anne T. M. Konkle, 2023. "The Influence of Social Media on the Perception of Autism Spectrum Disorders: Content Analysis of Public Discourse on YouTube Videos," IJERPH, MDPI, vol. 20(4), pages 1-15, February.

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    JEL classification:

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

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