Abstract
This paper focuses on autonomously classifying adverse events based on consumers’ comments regarding health and hygiene products. The data, comprising over 152,000 comments, were collected from e-commerce sources and social media. In the present research, we propose a language-independent approach using machine translation, allowing for unified analysis of data from various countries. Furthermore, this study presents a real-life application, making it potentially beneficial for subsequent scientific research and other business applications. A distinguishing feature of our approach is the efficient modeling of colloquial language instead of medical jargon, which is often the focus of adverse event research. Both hierarchical and non-hierarchical classification approaches were tested using Random Forest and XGBoost classifiers. The proposed feature extraction and selection process enabled us to include tokens important to minority classes in the dictionary. The F1 score was utilized to quantitatively assess the quality of classification. Hierarchical classification allowed for faster classification processes than the non-hierarchical approach for the XGBoost classifier. We obtained promising results for XGBoost; however, further research on a wider range of categories is required.
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Kaczorowska, M., Szymczak, P., Tkachuk, S. (2023). Hierarchical Classification of Adverse Events Based on Consumer’s Comments. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_17
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