Arfani, Alif Fauzi (2023) Sentiment Analysis of Public Views of the Qatar Government's Policy in the 2022 World Cup on Social Media Using the Naïve Bayes Method, Support Vector Machine Method and Random Forest Method. S1 Undergraduate thesis, UNIVERSITAS DARUSSALAM GONTOR.

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Abstract

Fifa World Cup or commonly called the World Cup is the biggest world football competition event. In 2022 this World Cup event was held in Qatar. The Qatari government implements or makes controversial policies that make many people issue stigmas or opinions that support, are disappointed or even don't care about the policies made, such as starting the Kafala system in the construction of stadiums which resulted in many people dying, alcohol prohibition, female tourists are required to wear hijab and a strict ban on Lesbi, Gay, Bisex, and Transgender (LGBT). This study aims to analyze the sentiment of public opinion towards the Qatari government's policies at the world cup event on social media twitter and youtube by classifying into positive, negative and neutral classes. In the classification stage, researchers used the Naive Bayes, Support Vector Machine and Random Forest methods. The classification results made using the Naïve Bayes method get an accuracy value of 77%, the results made using the Support Vector Machine method get an accuracy value of 79%, while the results of the Random Forest method get an accuracy value of 75%. In this research, it is concluded that the Support Vector Machine method has the highest accuracy value of 79% than the Naive Bayes and Random Forest methods.

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