A Novel Cognitive Multi-Label Classification Model for Social Media Data Based on Dolphin Optimized Learning and Hybrid Classification networks
DOI:
https://doi.org/10.22399/ijcesen.1737Keywords:
Extreme Multi-Label Classification, Dolphin Optimized Learning Model, Stacked Gated Recurrent Units, Feed Forward NetworksAbstract
Social media plays a pivotal role in people’s daily lives where users distribute diverse materials and subjects like ideas, events, and emotions. As the number of people grows, extensive use of social platforms has resulted in the creation of vast amounts of information. These unstructured data need to be labelled for understanding the relevant information that aids for various applications such as healthcare, entertainment and even sentimental politics. These unstructured data have large number of labels which needs the brighter light of annotation that tags the document with the most relevant labels. Extreme Multi-Label Classification (XMTC) aims to solve the above problem by automatically labelling a file with the most pertinent label from the large buckets of the large label sets. Because of the surge in big data, implementing the XMTC has become significant challenge to handle the millions of data, features and labels. This bottleneck was overshadowed with the arrival of Machine Learning (ML) and Deep Learning (DL) algorithms. But the computational overhead in training these learning networks degrades the performance of XMTC for handling the larger social media data. To solve this aforementioned problem, this research paper proposes the ensemble combination of Dolphin Optimized Learning and Hybrid classification networks. The proposed model comprises of triple set: Initially, it incorporates the multi-label dolphin optimized learning procedure to recognize the weight of every word in relation to labels. The label structure and document details are utilized to ascertain the link among the phrases and labels to compress the labels. Finally, the label-aware classification networks formulated with the Stacked Gated Recurrent networks and Feed Forward networks to attain the final label-aware massive documents. The comprehensive experimentation is carried out using the EuroLeX benchmarks and various performance metrics like accuracy, precision, recall, hamming score are calculated. To prove the excellence of the recommended XMTC with the varied state-of-the art models, Results demonstrates the proposed model has exhibited the superior performance over the models notably on the tail labels.
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