Cyberbullying Sentiment Analysis On Tweets In X (Twitter): A Case Study Using Support Vector Machine Method
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Abstract
The development of technology is currently experiencing very rapid progress, especially in the field of information and communication technology. The impact is so great that technology is considered an important part of everyday life. This has caused the internet to also experience major changes including in aspects of communication and information exchange. One example of technological progress is social media. The increasing number of internet users in Indonesia certainly means that there are also many social media platforms available and used by internet users to surf on the platform. Twitter (X) is a social media platform. Twitter (X) has become an effective means of spreading news and other content. Unwittingly, currently many social media users often cause various problems such as insults or defamation which is also known as cyberbullying. For this reason, this study analyzes sentiment with an approach process to identify and categorize opinions on certain topics or contexts from large data sources. By using Support Vector Machine (SVM) with the results of the Support Vector Machine method classification getting an accuracy value of 91%. The precision of the tested data results with the prediction results for the negative class is 91%, and the recall value used to measure the model's ability to predict negative data is 100% which shows that the model can detect all negative data perfectly. The results of the implementation of the sentiment prediction model using the SVM method that has been trained with a dataset that has positive and negative labeled sentences that can be effectively used to analyze sentiment in natural language texts for various purposes, such as detecting cyberbullying or analyzing public opinion.
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