  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.
