Abstract: For an existing cosmetic company to expand, it is crucial to understand customers’ opinions regarding cosmetic products through product reviews. Aspect-based sentiment classification (ABSC), which consists of text representation and classification stages, is typically employed to automatically extract the interested insights from review. Existing studies of ABSC primarily used single-label classification, which fails to capture relationships between multiple aspects in a review. Additionally, the use of contextual embeddings like IndoBERT for representing Indonesian-language cosmetic product reviews has been underexplored. This study addresses these issues by developing a multi-label classification model that leverages IndoBERT, including IndoBERT[b], IndoBERT[k], and IndoBERTweet, to better represent context and capture relationships across multiple aspects in a review. The model is trained and evaluated using a dataset of Indonesian-language cosmetic product reviews from Female Daily. The multi-label models can be constructed using IndoBERT directly as end-to-end model or employing IndoBERT solely as word embedding model. The latter model, also known as conventional multi-label model, needs to be coupled with problem transformation approach and classifier for classification. Single label classification model with Word2Vec serves as baseline to assess the improvement of multi-label model’s performance on Female Daily cosmetic product reviews dataset. The empirical results revealed that the multi-label approach was more effective in identifying sentiments for pre-defined aspects in reviews. Among the models, end-to-end IndoBERT[b] achieved the highest accuracy (86.98%), while conventional multi-label models combining IndoBERT[b], Label Powerset (LP), and Support Vector Machine (SVM) performed best with 69.64%. This study is significant as it provides a more generalized understanding of the BERT embedding within the context of multi-labels classification and explores the effect of contextual embedding in the cosmetic domain.

Ng Chin Mei, Sabrina Tiun and Gita Sastria, “Multi-Label Aspect-Sentiment Classification on Indonesian Cosmetic Product Reviews with IndoBERT Model” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151168

@article{Mei2024,
title = {Multi-Label Aspect-Sentiment Classification on Indonesian Cosmetic Product Reviews with IndoBERT Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151168},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151168},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Ng Chin Mei and Sabrina Tiun and Gita Sastria}
}