A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data

TL;DR

This paper compares NLP techniques and machine learning algorithms for sentiment analysis on US airline Twitter data, aiming to improve customer insight accuracy for better business strategies.

Contribution

It introduces the use of Bag-of-Words and TF-IDF with multiple ML classifiers for sentiment analysis on large, imbalanced datasets, highlighting effective combinations.

Findings

Support Vector Machine and Logistic Regression achieved 77% accuracy.

Bag-of-Words outperformed TF-IDF in this study.

The approach helps organizations better understand customer sentiment.

Abstract

Today's business ecosystem has become very competitive. Customer satisfaction has become a major focus for business growth. Business organizations are spending a lot of money and human resources on various strategies to understand and fulfill their customer's needs. But, because of defective manual analysis on multifarious needs of customers, many organizations are failing to achieve customer satisfaction. As a result, they are losing customer's loyalty and spending extra money on marketing. We can solve the problems by implementing Sentiment Analysis. It is a combined technique of Natural Language Processing (NLP) and Machine Learning (ML). Sentiment Analysis is broadly used to extract insights from wider public opinion behind certain topics, products, and services. We can do it from any online available data. In this paper, we have introduced two NLP techniques (Bag-of-Words and TF-IDF) and various ML classification algorithms (Support Vector Machine, Logistic Regression, Multinomial Naive Bayes, Random Forest) to find an effective approach for Sentiment Analysis on a large, imbalanced, and multi-classed dataset. Our best approaches provide 77% accuracy using Support Vector Machine and Logistic Regression with Bag-of-Words technique.