Sentiment analysis on twitter about the death penalty using the support vector machine method

Abstract

It is estimated that 175 million people in Indonesia utilize the Internet, according to the most recent We Are Social survey. 160 million of them are internet users who utilize social media, according to this data. It is estimated that 19.5 million Indonesians use Twitter. This is consistent with the numerous tweets that users have posted on Twitter about a variety of topics, including politics, music, health, and education. The death penalty is still one of the most popular subjects that is addressed on Twitter. When a judge rules that someone will be executed as retribution for a crime they have committed, this is referred to as the death penalty. As a result, sentiment analysis utilizing the Support Vector Machine technique with linear kernel features and Python programming was used to study public opinions on the death sentence. To improve the accuracy of the results obtained, data labeling on 848 data that were received through the scraping process was done manually in this study. Positive data is categorized as belonging to the class that supports the death sentence, while negative data is categorized as belonging to the class that opposes it. The study that was done shows an 8:2 difference between the training and test data. After preprocessing a dataset containing 758 data points, of which 606 will be utilized for training and 152 for testing, we obtain 91% accuracy, 91% precision, 100% recall, and 95% f1-score