Emotion-Aware Text Simplification Using Machine Learning and Generative AI

Beginning with how people understand tough passages, making them easier without losing meaning matters - particularly if strong feelings shape the message. What stands out here is a method designed to adjust wording while keeping emotion intact, not just cutting complexity. Instead of relying solely on traditional rules, it blends standard algorithms alongside modern artificial intelligence tools that produce human-like text. This combination helps balance clarity with emotional accuracy, fitting cases where mood influences understanding. A different approach begins with gathering labeled examples from Kaggle. Following collection, machine learning techniques guide the development of classifiers. Instead of using one algorithm, multiple options take shape - Logistic Regression appears alongside SVM and Random Forest. Before training, raw text undergoes cleaning steps, later turning into numerical features through TF-IDF conversion. Each system's outcome gets measured by checking correctness rates along with related indicators. Out of these trials, a top performer emerges. That version eventually runs live tests for detecting feelings as they happen. After detection of the emotion within the input text, users receive an option to pick how that feeling should be expressed. Though rewritten by a generative AI, the core message stays intact - only shaped now by the chosen emotional lens. With Streamlit, building interfaces that feel natural and responsive becomes quietly straightforward. Surprisingly, results show the system identifies feelings through precise recognition of emotional cues while generating clearer versions of text that reflect intended moods. Machine learning paired with generative models opens paths where text tools adapt more closely to what users actually need.