Intelligent Text Refinement through T5: Correction, Sentiment Insights, and Error Detection

This paper introduces a deep learning-based framework for simultaneous spelling and grammar correction with the T5 transformer model. The earlier methods for grammar correction were rule-based and statistical models, both of which were not robust enough in dealing with context-dependent, colloquial, or dialectal text. With advancements in Natural Language Processing (NLP), transformer architectures like T5 have shown better capabilities in reading and generating grammatically correct text with different linguistic structures. In this research, the T5 model is fine-tuned overa proprietary error-correction dataset and judged on syntactic correctness, semantic faithfulness, and contextual sensitivity. Our experimental results provide high performance scores (Accuracy: 93.5%, F1-score: 90.5%), validating the effectiveness of the model in fixing various linguistic inaccuracies. In-depth error analysis reveals challenges like formal language bias and misclassifications regarding slangs, negations, and short contexts. In addition, we measure bias pervasiveness across different parts of the NLP pipeline as well as provide actionable feedback for building more inclusive and context-sensitive grammar correction tools. This research advances toward creating wiser writing assistants that will meet more stringent standards as outlined by human editors without sacrificing linguistic diversity sensitivity.