Airbnb Price Prediction Using Machine Learning and Sentiment Analysis

@inproceedings{Kalehbasti2019AirbnbPP,
  title={Airbnb Price Prediction Using Machine Learning and Sentiment Analysis},
  author={Pouya Rezazadeh Kalehbasti and Liubov Nikolenko and Hoormazd Rezaei},
  booktitle={International Cross-Domain Conference on Machine Learning and Knowledge Extraction},
  year={2019}
}
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… 

Learning-based Airbnb Price Prediction Model

  • Siqi Yang
  • Business
    2021 2nd International Conference on E-Commerce and Internet Technology (ECIT)
  • 2021
Airbnb has become one of the largest online accommodation booking platforms today, providing more than 700 million accommodations in more than 220 countries. The main reason of successful booking is

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References

SHOWING 1-10 OF 15 REFERENCES

Real Estate Price Prediction with Regression and Classification CS 229 Autumn 2016 Project Final Report

The goal of this project is to create a regression model and a classification model that are able to accurately estimate the price of the house given the features.

Reasonable price recommendation on Airbnb using Multi-Scale clustering

This paper proposes the method of Multi-Scale Affinity Propagation aggregating the house appropriately by the landmark and the facility, and predicts the reasonable price, which is verified by the increasing number of the renting reviews.

Learning with self-attention for rental market spatial dynamics in the Atlanta metropolitan area

This work develops and evaluates rental market spatial dynamics models combining Long Short-Term Memory (LSTM) networks and self-attention mechanism, and uses techniques from saliency maps to explain the generated model.

Estimating Warehouse Rental Price using Machine Learning Techniques

Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size, and Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation.

LIBSVM: A library for support vector machines

Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

Opinion Mining on the Web 2.0 - Characteristics of User Generated Content and Their Impacts

This paper aims at identifying and determining differences and characteristics in opinion mining by performing an empirical analysis as a basis for a discussion which opinion mining approach seems to be applicable to which social media channel.

Application of deep learning to large scale riverine flow velocity estimation

This work uses three different forward solvers referred to as principal component analysis-deep neural network, supervised encoder, and supervised variational encoder to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of potential BCs.