A Cloud-Based Sentiment Analysis System with a BERT Algorithm for Fake News on Twitter

The rapid spread of the global COVID-19 pandemic has rapidly changed people’s communication demands and shifted them to digital channels, thus increasing the use of social networks more than ever. However, the increased use of social networks has also led to emotional confusion that has emerged with the fake news problem. As a result of limited studies on fine-grained sentiment analysis of fake news, this study comprehensively presents a sentiment analysis of fake news across seven main categories. For this reason, this study aims to address this problem with a cloud-based system called SA-ES using the BERT algorithm to understand the emotional dimension of fake news spreading on Twitter (now X). In this context, the sentiment analysis of fake news has been examined in seven categories. The SA-ES system consists of 248,262 training datasets and 10,202 test datasets for testing and evaluation. The SA-ES system was trained with the BERT algorithm in two epochs during the modeling phase and reached 99% accuracy. We hope that the development of this SA-ES system will fill a gap in the literature and also help take measures toward a healthier society by analyzing the moods of people who share fake news on social networks.