Online News Monitoring and Sentiment Analysis using BERT Approach

Abstract

Due to the advancement of Information Technology (IT), the medium for publishing news and events has gotten speedier. How people consume news has changed since the introduction of the Internet, online, and mobile technology. With the growth and evolution of digital media in today's society, Media Monitoring (MM) services have also become popular. While media monitoring interaction has obvious advantages, it also incurs considerable costs depending on the level of features it offers. Further, despite extensive research being done on sentiment analysis of social media and user input, there hasn't been much done so far to automatically predict the sentiment of narrative text. In this work, the proponent employed the pre-trained BERT model for the sentiment analysis feature and RSS feed to automate the collection and gathering of related online news in aid of the manual monitoring and analysis of online news stories and/or articles and avoid the costs associated with online media monitoring services. The developed system was able to produce appropriate features and a highly acceptable usability rating. The obtained results showed that the developed software will greatly improve and increase the end-user’s productivity by automating their existing manual system of monitoring and analysis of online news while reducing business costs by cutting the corresponding charges imposed by online media monitoring services.