Department of Computer Sciences

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https://repository.run.edu.ng/handle/123456789/72

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Browsing Department of Computer Sciences by Subject "Airline image branding"

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    Exploring the Performance Characteristics of Naive Bayes Classifier in the Sentiment Analysis of an Airline's Social Media Data
    (Advances in Science, Technology and Engineering Systems Journal, 2020) Oguntunde, Bosede
    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, catgorize 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 Naive bayes classifer 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 $i billion of the company's corporate value. Data were extracted from twitter, preprocessed and analyzed using the Naive 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 continous 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 customer could have a damaging effect on a brand and ultimately lead to a catastrophic loss in the organization.
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    Exploring the Performance Characteristics of the Naïve Bayes Classifier in the Sentiment Analysis of an Airline’s Social Media Data
    (Advances in Science, Technology and Engineering Systems Journal (ASTES), 2020) Odim, Mba
    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.