Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application

Abstract

PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.