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October 12, 2025Open Access

Sentiment Analysis of Social Media Data for Airline Brand Reputation Management Using Machine Learning Techniques in Python

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Authors

NRNeha Singh Rajput

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Overview

This analysis demonstrates how machine learning classifies sentiment in Twitter data, suggesting insights for airline management.

Key Points

  • XGBoost achieved superior classification accuracy in identifying airline customer sentiments.
  • The analysis utilized a dataset of 14,640 tweets categorized as positive, negative, or neutral sentiment.
  • Preprocessing techniques like tokenization and TF-IDF vectorization enhanced feature extraction and classification.
  • This study highlights challenges like sarcasm and informal language in accurately determining customer sentiment.

Cite This Study

Neha Singh Rajput (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff2472https://doi.org/10.20944/preprints202510.0782.v1
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  3. 3Advanced Natural Language Processing Techniques for Efficient Sentiment Analysis of US Airline Twitter Data: A High-Performance Framework for Extracting Insights from Tweets2024 · 3 citations
  4. 4Comparative Study Of Lexicon, Machine Learning, And Transformer-Based Models For Airline Sentiment Analysis2026
  5. 5A Comprehensive Study of Deep Learning and Traditional Machine Learning Models for Twitter Sentiment Analysis2025