The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification

Khyrina Airin Fariza Abu Samah - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka Kampus Jasin, Melaka, Malaysia
Nur Farhanah Amirah Misdan - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka Kampus Jasin, Melaka, Malaysia
Mohd Nor Hajar Hasrol Jono - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka Kampus Jasin, Melaka, Malaysia
Lala Septem Riza - Department of Computer Science Education, Universitas Pendidikan Indonesia, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.1.879

Abstract


Online reviews are crucial for business growth and customer satisfaction. There is no exception for the airlines’ company, which places third as the biggest contributor to Malaysia’s Gross Domestic Product. Customer opinions play an important role in maintaining the reputation and improving the quality of service of the airlines. However, there is no specific platform for online review. Most online ratings obtain English, leading to inaccurate results as not all reviews regarding different languages are considered. Airlines currently have no specific platform for online reviews despite being critical for business growth, performance, and customer experience improvement. Hence, this paper proposed implementing a web-based dashboard to visualize the best Malaysian airline companies. The airline companies involved are AirAsia, Malaysia Airlines, and Malindo Air. We designed and developed the proposed study through the bilingual analysis of Twitter sentiment using the Naïve Bayes algorithm. Naïve Bayes algorithm is a machine learning approach to do classification. The tweets extracted were analyzed as metrics that advance airline companies’ online presence. Testing phases have shown that the classifier successfully classified tweets’ sentiment with 93% accuracy for English and 91% for Bahasa. Every feature in the web-based dashboard functions correctly and visualizes a detailed analysis of sentiment. We applied the System Usability Scale to test the study’s usability and managed to get a score of 94.7%. The acceptability score ‘acceptable’ result concluded that the study reflects a good solution and can assist anyone in understanding the public views on airline companies in Malaysia.


Keywords


Bilingual model; classification; Naïve Bayes; Twitter sentiment analysis; web-based dashboard.

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