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September 10, 2025International Journal for Research in Applied Science and Engineering TechnologyOpen Access

A Comprehensive Study of Deep Learning and Traditional Machine Learning Models for Twitter Sentiment Analysis

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Authors

PTP. Sai Ravi Teja

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Overview

Comprehensive analysis compares deep learning and traditional models for sentiment analysis in Twitter data, suggesting optimal methods.

Key Points

  • BERT achieved the highest accuracy of 77.1% in sentiment analysis, indicating its effectiveness over other models.
  • Traditional machine learning methods like SVM and LSTM followed, with accuracies of 74.2% and 71.4%, respectively.
  • The study utilized the Twitter US Airline Sentiment dataset to implement and evaluate various algorithms for classification.
  • Insights into the strengths of each method provide guidance for selecting algorithms based on accuracy and computational needs.

Cite This Study

P. Sai Ravi Teja (2025) studied this question.

synapsesocial.com/papers/68c1a11f54b1d3bfb60dbbdahttps://doi.org/10.22214/ijraset.2025.73303
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