Sequential recommendation systems are vital for enhancing user experience and efficiency in e-commerce and online platforms by providing personalized suggestions. However, these systems often encounter challenges such as data sparsity and inaccuracy in identifying user preferences, leading to suboptimal recommendation quality. Existing solutions, like data augmentation and causal inference, have attempted to tackle these issues but have frequently neglected the crucial aspect of user feedback, which could potentially misrepresent user interests. In this paper, a new approach is introduced that incorporates a counterfactual classifier, textual information, and user feedback to enhance user-item interactions and the effectiveness of sequential recommendation systems. Our proposal is EUICC-SRec, a new methodology that incorporates a counterfactual classifier, a text module with bidirectional GRU channels, and a cross-attention mechanism. This method is designed to address data sparsity and enhance the precision of sequential recommendations by improving accuracy and relevance. The proposed approach has been evaluated through extensive experiments, demonstrating its superiority in mitigating data sparsity issues and accurately capturing user preferences compared to existing methods.