Topic Modeling and Sentiment Analysis of Online Review for Airlines

The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better...

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Bibliographic Details
Published in:Information (Basel) 2021-02, Vol.12 (2), p.78
Main Authors: Kwon, Hye-Jin, Ban, Hyun-Jeong, Jun, Jae-Kyoon, Kim, Hak-Seon
Format: Article
Language:English
Subjects:
Airline industry
Airlines
Asian airlines
Aviation
Big Data
Competition
Competitive advantage
Customer satisfaction
Customer services
Customers
Data analysis
Data collection
Data mining
Decision making
Frequency analysis
Hospitality industry
Information storage
Internet
Literature reviews
Marketing
Modelling
online review
Profitability
Profits
Quality of service
Research methodology
Sentiment analysis
text mining
topic modeling
Tourism
User generated content
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Summary:The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12020078