Decoding Guest Experiences: NLP-Driven Sentiment Analysis of Hotel Reviews

This project presents a real-time sentiment and emotion analysis system designed to decode guest experiences from hotel reviews. Customer reviews contain rich, unstructured textual information that reflects satisfaction, complaints, expectations, and emotional states. Traditional manual review analysis is slow, inconsistent, and prone to human bias. To address this challenge, the proposed system applies Natural Language Processing (NLP), machine learning, and deep learning techniques to automatically classify emotions such as happiness, sad, satisfaction .

The system performs preprocessing steps including stop- word removal, tokenization, lemmatization, punctuation cleaning, and text normalization. Feature extraction is carried out using TF-IDF and word embeddings (Word2Vec, GloVe, BERT). A hybrid sentiment– emotion classification model combining machine learning classifiers (SVM, Random Forest) and deep learning models (LSTM, BERT Transformer) provides high-accuracy results. The proposed system visualizes emotion distribution, detects aspect-based sentiments (staff, cleanliness, amenities), and identifies trends over time.

This NLP-driven approach accurately converts unstructured text into meaningful emotional insights, enabling hotels to improve service quality, detect negative sentiment early, enhance customer satisfaction, and make data-driven decisions.