Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets

TL;DR

This paper presents a machine learning-based method to analyze airline passenger sentiment from Twitter data, combining sentiment classification, lexical analysis, and time series techniques to detect significant sentiment shifts.

Contribution

It introduces an integrated approach that combines sentiment analysis, lexical keyword modeling, and abnormality detection in sentiment trends from Twitter data.

Findings

Effective detection of sudden sentiment changes in airline passenger tweets

Demonstrated capability to monitor customer satisfaction in real-time

Potential application for airline companies to manage customer relations

Abstract

As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach. Relevant Tweets are retrieved from Twitter's API and processed through tokenization and vectorization. After that, these processed vectors are passed into a pre-trained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also perform lexical analysis on the collected Tweets to model keywords' frequencies, which provide meaningful contexts to facilitate the interpretation of sentiments. We then apply time series methods such as Bollinger Bands to detect abnormalities in sentiment data. Using historical records from January to July 2022, our approach is proven to be capable of capturing sudden and significant changes in passengers' sentiment. This study has the potential to be developed into an application that can help airlines, along with several other customer-facing businesses, efficiently detect abrupt changes in customers' sentiments and take adequate measures to counteract them.