  In this study, we compared the performance of four different methods for
multi label text classification using a specific imbalanced business dataset.
The four methods we evaluated were fine tuned BERT, Binary Relevance,
Classifier Chains, and Label Powerset. The results show that fine tuned BERT
outperforms the other three methods by a significant margin, achieving high
values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also
performs well on this dataset, while Classifier Chains and Label Powerset
demonstrate relatively poor performance. These findings highlight the
effectiveness of fine tuned BERT for multi label text classification tasks, and
suggest that it may be a useful tool for businesses seeking to analyze complex
and multifaceted texts.

Information Retrieval;Machine Learning