ANALISIS SENTIMEN TERHADAP GAME GENSHIN IMPACT MENGGUNAKAN BERT - Ryo Kusnadi; Yusuf Yusuf; Andriantony Andriantony; Richard Ardian Yaputra; Melna Caintan (LPPM Universitas Abdurrab, 2021)
By huge improvement of Internet Services on social networking, there are a lot data that were streamly made in every time. Recently, sentiment analysis by using online reviews and messages has become a popular research issue in Natural Langauage Processing field. Over the years, online game have become inseparable thing for most of us, especially in the widespread economic disruption caused by the Covid-19. Genshin Impact is one of the well-known game that developed by miHoYo. This research focused on sentiment analysis with the purpose to find out whether the respected review that scraps from google play store has a neutral, positive or negative sentiment so it will be helpful for afterward game improvement. An autonomus sentiment analysis classification process is required to reduce human error. But, it is hard to get studies that explore about the extraction features and the deep learning models that fit with this case, especially in the business game. This research process stage is data scrapping through the google play store, and using Bidirectional Encoder Representations from Transformers (BERT) as the machine learning model.