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This study provides a comprehensive review of machine learning techniques applied to sentiment analysis, focusing on comparing the strength and performance of each technique in handling data sets. In this study, several machine learning techniques are used to conduct sentiment analysis, namely supervised learning, ensemble learning, and deep learning. Through the analysis carried out, this study provides in-depth insights for researchers and practitioners about the advantages and disadvantages of each technique applied in the context of opinion digging. The findings show that deep learning has an overall superior performance compared to other techniques, especially in terms of accuracy and ability to handle complex data. The results of this study are expected to be a reference for further development in the field of sentiment analysis and the application of machine learning techniques in sentiment analysis derived from social media data.

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Advancing Sentiment Analysis on Social Media: A Comparative Study of Supervised Learning, Deep Learning, and Ensemble Learning Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • Nathifa Agustiana,
  • Muharman Lubis,
  • Hanif Fakhrurroja,
  • Putri Utami Rukmana,
  • Asriana,
  • Anggraeni Xena Paradita

摘要

This study provides a comprehensive review of machine learning techniques applied to sentiment analysis, focusing on comparing the strength and performance of each technique in handling data sets. In this study, several machine learning techniques are used to conduct sentiment analysis, namely supervised learning, ensemble learning, and deep learning. Through the analysis carried out, this study provides in-depth insights for researchers and practitioners about the advantages and disadvantages of each technique applied in the context of opinion digging. The findings show that deep learning has an overall superior performance compared to other techniques, especially in terms of accuracy and ability to handle complex data. The results of this study are expected to be a reference for further development in the field of sentiment analysis and the application of machine learning techniques in sentiment analysis derived from social media data.