A Social Media Sentiment Analysis Using Machine Learning Approaches

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Analysis shows 93% accuracy in sentiment classification using Random Forest algorithms on Twitter data, indicating machine learning effectiveness.

The Random Forest model achieved a high accuracy of 93% in classifying sentiments on Twitter posts, demonstrating its effectiveness.
Machine learning algorithms, particularly the Random Forest model, were evaluated for their ability to analyze social media sentiments effectively.
Feature extraction methods like TF-IDF and Natural Language Processing were applied to enhance sentiment analysis accuracy.
This research highlights a gap in the analysis of sentiment on social media, emphasizing potential for further studies in this area.

Al-Agele et al. (2025) studied this question.

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View Full Paper

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Authors

Analysis shows 93% accuracy in sentiment classification using Random Forest algorithms on Twitter data, indicating machine learning effectiveness.

The Random Forest model achieved a high accuracy of 93% in classifying sentiments on Twitter posts, demonstrating its effectiveness.
Machine learning algorithms, particularly the Random Forest model, were evaluated for their ability to analyze social media sentiments effectively.
Feature extraction methods like TF-IDF and Natural Language Processing were applied to enhance sentiment analysis accuracy.
This research highlights a gap in the analysis of sentiment on social media, emphasizing potential for further studies in this area.

Al-Agele et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark