Exploring the Performance Characteristics of the Naïve Bayes Classifier in the Sentiment Analysis of an Airline’s Social Media Data
Volume 5, Issue 4, Page No 266-272, 2020
Author’s Name: Mba Obasi Odima), Adewale Opeoluwa Ogunde, Bosede Oyenike Oguntunde, Samuel Ayodele Phillips
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Department of Computer Science, Redeemer’s University, Ede 23210, Nigeria
a)Author to whom correspondence should be addressed. E-mail: odimm@run.edu.ng
Adv. Sci. Technol. Eng. Syst. J. 5(4), 266-272 (2020); 
DOI: 10.25046/aj050433
Keywords: Airline image branding, Naïve Bayes, Sentiment analysis
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Airline operators get much feedback from their customers which are vital for both operational and strategic planning. Social media has become one of the most popular platforms for obtaining such feedback. However, to analyze, categorize, and generate useful insight from the huge quantity of data on social media is not a trivial task. This study investigates the capability of the Naïve Bayes classifier for analyzing sentiments of airline image branding. It further examines the impact of data size on the accuracy of the classifier. We collected data about some online conversations relating to an incident where an airline’s security operatives roughly handled a passenger as a case study. It was reported that the incident resulted in a loss of about $1 billion of the company’s corporate value. Data were extracted from twitter, preprocessed and analyzed using the Naïve Bayes Classifier. The findings showed a 62.53% negative and 37.47% positive sentiments about the incident with a classification accuracy of over 0.97. To assess the impact of training size on the accuracy of the classifier, the training sets were varied into different sizes. A direct linear relationship between the training size and the classifier’s accuracy was observed. This implies that large training data sets have the potentials for increasing the classification accuracy of the classifier. However, it was also observed that a continuous increase in the classification size could lead to overfitting. Hence there is a need to develop mechanisms for determining optimum training size for finest accuracy of the classifier. The negative perceptions of customers could have a damaging effect on a brand and ultimately lead to a catastrophic loss in the organization.
Received: 20 April 2020, Accepted: 08 July 2020, Published Online: 28 July 2020
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