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 **SentiNet🤖** is an experimental project exploring different approaches to sentiment classification, with a focus on handling nuanced language phenomena such as sarcasm, shifting tones, and negation. 
 By fine‑tuning the Microsoft DeBERTa‑v3 encoder and comparing it against classic machine learning baselines and recurrent models, SentiNet demonstrates how modern Transformers capture contextual meaning beyond word‑level cues.
 The system highlights the strengths and weaknesses of each approach while providing an interactive demo that outputs clear sentiment labels (😀 Positive / 😞 Negative) alongside confidence scores, making evaluation both rigorous and accessible.  
-Project GitHub: [https://github.com/HooMAN/SentiNet](https://github.com/HooMAN/SentiNet)
+Project GitHub: [https://github.com/Hoom4n/SentiNet](https://github.com/Hoom4n/SentiNet)