BERT Implementation on News Sentiment Analysis and Analysis Benefits on Branding

Abstract

The rapid development of information makes data processing easy and fast, especially in the business world, so many business brands have used the internet as a marketing medium for their operations. Now the business does not only depend on its operations; now, the opinion of the public media, especially on the news, has become an essential spotlight in today's business, especially against negative opinions that indirectly impact the image and product branding of the business, we need the proper means to help identifying and analyzing this kind of news. This study aims to identify and analyze sentiment with negative and positive indications on news titles from one of the sources of an Indonesian online news portal using the Bidirectional Representations from Transformers (BERT) sentiment analysis method, with the measurement of the confusion matrix metrics to measure and identify which headlines contains negative and positive indications. The sentiment analysis system offers identification and categorization with ease and immediately provide good results on identifying news. The results of this study, the sentiment model achieves an accuracy rate of 93% in identifying negative and positive news and F1-Score on negative identification rate of 92% and positive identification rate of 93%. The sentiment analysis system was built as effort to help analyzing against positive news indications or awful news as analysis benefits carried out to identifying alarming news indications towards branding.