Granular Computing based Deep learning for Text Classification

Granular computing involves a comprehensive process that encompasses theories, methodologies, and techniques to solve complex problems, rather than being just an algorithm. As the volume of generated data continues to grow rapidly, data-driven problems have become increasingly complex. Although deep learning models have outperformed traditional machine learning models in solving complex problems, there is still room for enhancing their performance. In this paper, we propose a granular computing-based deep learning model, aimed at enhancing classifier accuracy in complex natural language-based problems. The proposed approach involves a new granulation method, which comprises a novel algorithm built on combinatorial concepts and ten rule-based numerical granules. By utilizing this granulation method, each granule adds a new representation and concept to the existing data. The proposed model consists of multiple models that perform learning separately in a granular view. In the final step, the model pays attention to the granulated matrices generated by various models. The proposed model is evaluated using datasets related to cyberbullying and two hate speech, yielding results that demonstrate significant accuracy enhancements compared to state-of-the-art models.