Sentiment Analysis of Hoax News Job Vacancy With Naive Bayes Method

Abstract

Abstract

Hoax news or fake news has become a significant problem in today's digital era, the news in question is information on job vacancies on social media, one of which is Twitter, which is widely discussed by job seekers. The spread of hoax news related to job vacancies can cause harm to job seekers who rely on this information to find work. Therefore, sentiment analysis of hoax job vacancies is important to help users distinguish between valid and hoax information. This study aims to analyze sentiment towards hoax news related to job vacancies on Twitter. The method used is the Naive Bayes method, which is a classification method commonly used in sentiment analysis. Hoax news dataset collected from job vacancy information will be processed and features that  are  relevant  to  job vacancies will  be identified. Furthermore, the Naive Bayes sentiment analysis model will be developed to classify hoax news sentiment as positive or negative. The results of sentiment analysis will help users identify and avoid hoax news related to job vacancies. In addition, the results of sentiment analysis also improve the quality of the content displayed and strengthen user confidence in the information provided. It is hoped that this research will contribute to overcoming the problem of hoax news on Twitter, improve the quality of available information, and assist job seekers in making better decisions in finding work.