Hierarchical multi-class classification of imbalanced product descriptions using Support Vector Machines and BERT

Product data can be useful to perform environmental impact assessments of prod-uct life-cycles. In order to automatize such assessments, this research is examiningmethodologies that encounter the challenges with the processing and classification ofproduct data. We consider a large imbalanced and multilingual dataset with short andnoisy product descriptions that have been labeled by human annotators. The productclasses are hierarchically ordered and characterized by two levels. To treat the class im-balance we proposed two data enrichment methods on the training data. Oversamplingand a web scraping method with a prior-filtering. The web scraper was parsing webdata using a search engine and used Sentence-BERT with cosine-similarity to assesssemantic relevant information. In addition, we proposed two classification methods,Support Vector Machines (SVM) and BERT. Both models were evaluated according toseveral experiments considering a flattened- and hierarchical classification approach...Show moreProduct data can be useful to perform environmental impact assessments of prod-uct life-cycles. In order to automatize such assessments, this research is examiningmethodologies that encounter the challenges with the processing and classification ofproduct data. We consider a large imbalanced and multilingual dataset with short andnoisy product descriptions that have been labeled by human annotators. The productclasses are hierarchically ordered and characterized by two levels. To treat the class im-balance we proposed two data enrichment methods on the training data. Oversamplingand a web scraping method with a prior-filtering. The web scraper was parsing webdata using a search engine and used Sentence-BERT with cosine-similarity to assesssemantic relevant information. In addition, we proposed two classification methods,Support Vector Machines (SVM) and BERT. Both models were evaluated according toseveral experiments considering a flattened- and hierarchical classification approach ofthe products. In addition, we perform an extensive error analysis on the model resultsconsidering the SVM feature importance and the BERT attention weights.The results showed that both models show similar flattened classification performanceusing the normal data, i.e. no data enrichment. SVM show better flattened classi-fication performance after treated the class imbalance with data enrichment. BERTshow poor performance using data enrichment and is overfitting the training data.Hierarchical classification improved the classification performance of BERT using over-sampling. SVM did not benefit from the hierarchical classification approach and showbetter classification performance using flattened classification. As last, the error anal-ysis have showed that the data consist of incorrect or subjective manual labeling.The SVM feature importance and BERT attention weights results suggest that non-representative tokens or out-of-vocabulary tokens have the tendency to decrease theclassification performance.Show less

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Hierarchical multi-class classification of imbalanced product descriptions using Support Vector Machines and BERT