The Science of Emotion: Malaysian Airlines Sentiment Analysis using BERT Approach

Abstract

Sentiment analysis has grown to be one of the most active research areas in Natural Language Processing (NLP). Sentiment analysis, also known as opinion mining, uses a series of methods, techniques and tools to study people’s opinions, views and sentiment towards a wide range of topics such as products, services, events and issues. In the airline industry, millions of people today use social networking sites such Twitter, Skytrax, TripAdvisor to express their emotions, opinions, and share information about the aircraft service. It is a hidden gem to the airline company to gain valuable insight from this data and have the broadest possible view into what people are saying about the airline’ brand online. Hence, this paper explores six different sentiment analysis models: Random Forest, Multinomial Naive Bayes, Linear Support Vector Classifier, Ensemble Method, Bidirectional Long Term Short Memory (Bi-LSTM) and BERT model, in order to determine and develop the best model to be used. The best model was then used to determine the social status, company reputation, and brand image of Malaysian airline companies. In conclusion, the BERT model was found to perform the best out of the six models tested, scoring an accuracy of 86%. Keywords: Supervised Learning, Ensemble Learning, Deep Learning, Transfer Learning, Airline Sentiment