Nimay12345

I need an NLP workflow that ingests social media posts, customer reviews, and email messages, then detects the dominant emotion—Happy, Sad, or Angry—in each piece of text. The solution should be reliable enough for daily, automated processing and simple to retrain as my data grows.

I already have raw text stored in CSV files; you can assume each record contains a unique ID and a text field. Your job is to:

• Clean and normalise the text (language is English only).
• Build or fine-tune a model that classifies the emotion with high accuracy.
• Expose the results through a concise report (precision, recall, F1) and a small demo script or notebook that shows end-to-end usage.

Python, spaCy, Hugging Face, or similar libraries are welcome—as long as the final code is readable, documented, and ready to run on my local machine (standard CPU).