Multilingual Deception Detection of GPT-generated Hotel Reviews
Modern hotel review sentiment analysis with interactive GUI, AI explanations, and educational features. Python/ML/NLP project.
Transformer-based modeling of hotel review text to predict star ratings (1–5), evaluate ordinal classification performance, and analyze sentiment separability through embedding-based clustering.
Wanderlust is a modern hotel renting platform built with Node.js, Express.js, MongoDB, and EJS, designed to deliver an intuitive and seamless experience for travelers and hotel owners alike.
This project analyzes 1,500 customer reviews from Booking.com for La Veranda Hotel (Larnaca, Cyprus) using Natural Language Processing (NLP). It performs sentiment scoring, topic modeling (LDA), and geographic sentiment analysis to uncover actionable insights that can improve hospitality operations and marketing strategy. Built using Orange.
Sentiment Analysis of hotel reviews by using Word Embedding, CNN, RNN
Software that fits how you actually work. Makers of https://www.reviato.com
Text summarization of Hotel reviews using Huggingface Transformers T5 and Bart
Scraping hotel reviews from TripAdvisor pages - Final Project for CSCI 2930 Unix Tools
Turn hotel guest reviews into actionable insights. Scrapes ratings and reviews from Booking.com, TripAdvisor, Google, Expedia, and HolidayCheck, classifies them by topic and sentiment, and visualizes trends in a Streamlit dashboard.
Software that fits how you actually work. Makers of https://www.reviato.com
Priceline hotel reviews extraction
Hotel IQ is a full-stack platform for analyzing hotel reviews and business data. Built with Angular 19, FastAPI, Power BI, and AI (OpenAI GPT-4o) for sentiment analysis. Visualize KPIs, explore reviews, and generate customer satisfaction insights. 100% French interface.
HRS Reviews hotel analytics
An exercise using Python in hotel review data to determine the qualities of a hotel stay that contribute to greater customer satisfaction and higher ratings for a fictitious Hotel Management Inc.
Develops a supervised machine learning pipeline to predict hotel review scores using structured metadata. Includes exploratory analysis, feature engineering, and model evaluation across Linear Regression, Random Forest, and XGBoost. Designed for reproducibility, interpretability, and portfolio presentation.
Advanced ML-based sentiment analysis and customer segmentation for 515k+ European hotel reviews. Identifies hidden patterns in customer satisfaction using NLTK VADER and K-Means clustering.
ML project for hotel review sentiment analysis with feature engineering and model interpretation.
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