Comparison Study on Sentiment Analysis Using Lexicon for Airlines Using Supervised Methods

Abstract

Nowadays, sentiment analysis usually uses social media websites such as Twitter to analyse the public's opinion on a particular topic. Users have unrestricted access to this website and can express their opinions freely without any restrictions, and it is well-known that opinions influence readers. Therefore, the main objective of this research is to identify the public's positive, negative, and neutral attitudes towards airlines such as Malaysian Airlines, Air Asia, and Malindo Air. Two approaches are adopted: the lexicon-based approach to label the tweets and the machine learning approach such as Naïve Bayes, SVM, and Deep Learning to predict and compare the performance. A total of 35,005 tweets from airlines with all three keywords were evaluated. Deep Learning achieved the highest accuracy and f1 score with 74.10% and 73.49%, respectively. The results show that Deep Learning outperforms the other classifiers by having the highest precision and f1 score. Finally, the sentiment analysis results are visualized in a dashboard to enable a more accurate research analysis. For future work, the dashboard could be integrated into a web-based dashboard to be published for the public and not only for airlines.