Visualizing the best Malaysian Airline Companies through Twitter sentiment analysis using Naïve Bayes / Khyrina Airin Fariza Abu Samah ... [et al.]

In Malaysia, 37.1% of Internet users owned Twitter accounts in 2020. Besides that, the tourism industry is the third biggest contributor to Malaysia, putting the aviation and travel industry as part of the category. However, there is no specific platform for direct comparison for the online reviews among companies despite it is critical for business growth, performance and improvement of customer experience. Other than that, most online ratings obtained their result from the online platform using the English language only. Thus, this study aims to visualize the best Malaysian airline companies through Twitter sentiment analysis using Naïve Bayes (NB). The source of the data for this project is Twitter, where the tweets are extracted using dates and keywords. The data was pre-processed, and the model is run on real-world data. The model evaluation is conducted using the NB classifier. Two machine learning models for English and Bahasa Malaysia have been built for classification purposes based on the multi-class text classification. The results obtained are visualized in a dashboard. High accuracy score is achieved during testing and the project objectives are achieved. The future work that can be put into this project is to include other social media platforms for a wide reach to the companies.