| dc.description.abstract | Product reviews can be information for potential consumers to find out about the
product to be purchased. Reviews can be useful as a reference and consideration for
consumers before buying a product. There are various methods for providing product
reviews, namely through writing, social media, photos and videos or a combination of
various media. A widely used method is to provide reviews through writing. Someone
can give their opinion and can be analyzed to find out the type of review submitted
including neutral sentences, complaints suggestions or praise. Thus, this research was
conducted to classify the types of reviews contained in the text. In this study, the BERT
(Bidirectional Encoder Representations from Transformers) approach method was
used. 2000 data in the form of beauty product reviews collected through the SOCO
Review site were used in the study. Then the dataset is annotated into 4 classes of
review types, namely neutral, complaints, suggestions, and praise before entering the
classification process. The pre-processing stages in this study include case folding,
data cleaning, tokenization, stopword removal, and normalization. Model building
uses the BERTBASE architecture, namely IndoBERT-base-p1. The hyperparameters
used in this research are batch size 32, learning rate 3e-6, and 5 epochs. The results
achieved in this study resulted in a fairly good accuracy of 90%. | en_US |
