Efficient Deep Learning Models for Extreme Multi- Label Text Classification - Review Paper

While social media has enabled the broad dissemination of user perspectives, it has also generated an immense volume of unstructured text requiring sophisticated categorization techniques. This comprehensive literature review explores advances in deep learning mod-els tailored for extreme multi-label text classification of social media content. Significant progress includes the development of neural architectures like convolutional networks, re-current networks, and transformer models such as BERT, markedly improving the scala-bility, accuracy, and interpretability of classification tasks. Transformer models like BERT have transformed the field by facilitating a richer comprehension of multifaceted social media posts, enabling more precise label predictions. However, classifying social media presents unique difficulties due to its high dimensionality, and sparse and imbalanced distributions that challenge model training. Emerging approaches address these hurdles, such as label partitioning, attention mechanisms, and hybrid architectures, strengthening model robustness and efficiency. Nonetheless, building interpretable, real-time applicable systems capable of adapting to social media's dynamic nature remains an ongoing challenge. This analysis provides a thorough overview of the deep learning state-of-the-art for multi-label classification and outlines future directions to resolve lingering issues in this rapidly advancing domain. The findings are intended to guide next-generation model development better suited to social media's intricate classification demands.