Airbnb Price Prediction Using Machine Learning and Sentiment Analysis
This work addresses the challenge of price evaluation for Airbnb property owners and customers, but it appears incremental as it applies existing methods to a specific domain without novel methodological breakthroughs.
The paper tackled the problem of predicting Airbnb rental prices by developing a model using machine learning, deep learning, and natural language processing techniques, with features including rental characteristics, owner details, and customer reviews, but no concrete performance numbers were provided in the abstract.
Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.