Real-Time Extracted Hotel Reviews Analysis Using Logistic Regression

In this study, we focus on real-time analysis of hotel reviews using logistic regression. The reviews are directly extracted from Google through web scraping, utilizing Selenium in headless browser mode. Once collected, the reviews undergo preprocessing steps including tokenization, stop word removal, and TF-IDF vectorization. The cleaned and vectorized data is then used to train a logistic regression model, which classifies the reviews as either positive or negative. The classification results are displayed in real-time through a Streamlit-based user interface, enabling users to instantly view the sentiment of reviews for any hotel. This system aims to assist users in making more informed decisions when selecting hotels based on authentic customer feedback. Logistic regression was selected for its simplicity, interpretability, and strong performance in binary classification tasks. I. TITLE Real Time Extracted Hotel Review Analysis Using Logistics Regression.