Step	Description	Output
1. Planning & Objectives	Defined sentiment analysis goals and KPIs with marketing and support teams.	Clear goals to understand customer emotions and feedback.
2. Data Collection	Customer reviews and feedback Social media posts and comments Support tickets and chat transcripts	Comprehensive dataset of text-based customer feedback.
3. Data Cleaning & Preparation	Removed irrelevant data and noise Tokenized and normalized text for NLP	Cleaned text dataset ready for NLP modeling.
4. NLP Modeling	Applied natural language processing models to classify sentiment (positive, neutral, negative).	Model identifying sentiment polarity for each text record.
5. Visualization & Insights	Dashboards showing sentiment trends Insights for customer experience improvement	Actionable insights for marketing and support teams.
6. Testing & Feedback	Model accuracy checks Refinements based on stakeholder feedback	Reliable sentiment analysis for decision-making.
Final Output	Fully integrated sentiment analysis dashboard for real-time insights.	Improved customer experience Better marketing campaigns Faster resolution of customer issues