AI-Powered Sentiment Analysis for Enhancing Guest Experience in the Hospitality Industry

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.