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September 10, 2025Engineering Technology & Applied Science ResearchOpen Access

Efficiency Determination of Various Machine Learning Techniques for Sentiment Analysis on Social Media Platforms

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Authors

RKRachita KansalCDChander Diwaker

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Overview

This review assesses machine learning performance metrics in sentiment analysis on social media, highlighting unstructured data challenges.

Key Points

  • Machine learning techniques significantly improve sentiment analysis accuracy on social media data, enhancing insights.
  • The study showcases various performance metrics, including F1-score and precision, assessing machine learning effectiveness.
  • A comprehensive review of deep learning models highlights advancements in sentiment detection and text mining capabilities.
  • Addressing unstructured data challenges reveals the limitations of traditional methods for sentiment analysis applications.

Cite This Study

Kansal et al. (2025) studied this question.

synapsesocial.com/papers/68c24009b210217d64799219https://doi.org/10.48084/etasr.11158
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  1. 1A Social Media Sentiment Analysis Using Machine Learning Approaches2025
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  3. 3Analysis of challenges of automatic sentiment detection methods in unstructured texts2025
  4. 4The Use of Machine Learning in Social Media Sentiment Analysis: Communication Strategies in The Digital Age2025
  5. 5Consumer Behavior and Purchase Intentions: A Machine Learning-Based Sentiment Analysis of Social Media Data2024