English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As one of the most extensive social networking services, Twitter has morethan 300 million active users as of 2022. Among its many functions, Twitter isnow one of the go-to platforms for consumers to share their opinions aboutproducts or experiences, including flight services provided by commercialairlines. This study aims to measure customer satisfaction by analyzingsentiments of Tweets that mention airlines using a machine learning approach.Relevant Tweets are retrieved from Twitter's API and processed throughtokenization and vectorization. After that, these processed vectors are passedinto a pre-trained machine learning classifier to predict the sentiments. Inaddition to sentiment analysis, we also perform lexical analysis on thecollected Tweets to model keywords' frequencies, which provide meaningfulcontexts to facilitate the interpretation of sentiments. We then apply timeseries methods such as Bollinger Bands to detect abnormalities in sentimentdata. Using historical records from January to July 2022, our approach isproven to be capable of capturing sudden and significant changes in passengers'sentiment. This study has the potential to be developed into an applicationthat can help airlines, along with several other customer-facing businesses,efficiently detect abrupt changes in customers' sentiments and take adequatemeasures to counteract them.

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