Problem: Hotels and restaurants struggle to analyze guest feedback effectively, leading to missed opportunities for service improvement.
Outcome: Develop an AI-powered sentiment analysis system to extract and analyze customer feedback from multiple sources.
Week 1-2: Data Collection & Preprocessing
Gather guest reviews from TripAdvisor, Google, and booking platforms.
Clean and preprocess text data for sentiment analysis.
Week 3-4: Sentiment Analysis Model Development
Implement NLP techniques (TF-IDF, Word2Vec).
Train machine learning models (Naïve Bayes, LSTM) for sentiment classification.
Week 5-6: Model Evaluation & Optimization
Compare model performance using precision, recall, and F1-score.
Fine-tune model parameters for accuracy improvement.
Week 7-8: Dashboard & Visualization Development
Build interactive sentiment dashboards using Tableau/Power BI.
Implement keyword-based insights for service improvement.
Provide recommendations for improving guest satisfaction.
Implement an automated feedback response system.
Week 11-12: Report & Deployment
Document findings and business impact.
Deploy sentiment analysis tool in a cloud-based environment.
