Abstract
Twitter is one of the world social media giants, which has enormous flow text-based comment in everysecond. There are many types of writing sentiments that users create to discuss something such as famousfigures, companies or politics. One of the types is hate speech. The analysis was carried out using theSupport Vector Machine method as a text analysis model with the help of TF-IDF to assess the weight ofeach word. The experiment was carried out with several types of kernels and resulted in varying degrees ofaccuracy. The types of kernels tested were linear, radial basis function, polynomial and sigmoid with a testdata distribution of 20%, 25% and 30%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
