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Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on

Ganesh Kumar Wadhwani1, Pankaj Kumar Varshney1, Anjali Gupta1

  • 1Department of Computer Science, IITM, GGSIPU, New Delhi, India.

SN Computer Science
|May 1, 2023
PubMed
Summary
This summary is machine-generated.

This study analyzes public perception of the Russia-Ukraine war using machine learning on 11,250 tweets. The Extra Trees Classifier with Bag-of-Words achieved 0.84 accuracy, demonstrating effective sentiment analysis.

Keywords:
Feature engineeringMachine learningSentiment analysisSupervised machine learning modelsText classification

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Machine Learning

Background:

  • The Russia-Ukraine War, escalating in February 2022, has generated significant global public discourse.
  • Social media platforms like Twitter are crucial for disseminating public opinion during geopolitical crises.
  • Understanding public perception is vital for analyzing the socio-political impact of the conflict.

Purpose of the Study:

  • To examine public perceptions of the Russia-Ukraine War.
  • To leverage machine learning for analyzing sentiment and opinions expressed in social media data.
  • To evaluate the effectiveness of different machine learning models and feature extraction techniques for text analysis.

Main Methods:

  • Utilized a dataset of 11,250 tweets related to the Russia-Ukraine War.
  • Applied Natural Language Processing (NLP) techniques, including sentiment analysis and entity annotation.
  • Developed and tested machine learning models using TF-IDF, Bag-of-Words (BoW), and N-gram feature extraction.
  • Compared performance using metrics such as accuracy, precision, recall, and F1-score.

Main Results:

  • The Extra Trees Classifier (ETC) model achieved the highest accuracy of 0.84 when combined with the Bag-of-Words (BoW) feature extraction method.
  • Evaluated and compared various machine learning algorithms including Logistic Regression, Decision Tree, SVM, XGB, Gaussian Naive Bayes, ADA, and KNN.
  • Demonstrated the efficacy of machine learning in classifying emotions and analyzing textual polarity and subjectivity.

Conclusions:

  • Machine learning models, particularly the ETC with BoW, are effective for analyzing public sentiment on geopolitical events from social media data.
  • NLP and text analytics provide powerful tools for understanding large-scale public opinion during international conflicts.
  • The study highlights the potential of computational methods in social science research for crisis analysis.