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

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.

Neha Singh Rajput (2025) studied this question.

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October 12, 2025Open Access

View Full Paper

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Authors

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

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.

Neha Singh Rajput (2025) studied this question.

View Full Paper

Ask AI

Bookmark