Project Title:

Airbnb Review Dynamics: Correlation between the Number of Reviews and Other Factors Among Airbnb Listings

Project Description:

This project investigates how factors, such as amenities and description sentiment scores, influence the number of positive reviews on Airbnb listings in San Francisco. Our analysis helps Airbnb hosts optimize their listings to enhance guest experiences and improve competitiveness in a saturated market.

Key Contributions:

●     Employed advanced data analytics techniques, including text mining, sentiment analysis, and linear regression, to assess the impact of listing features on guest reviews.

●     Identified essential amenities and descriptive elements that significantly influence positive guest reviews, providing actionable insights for hosts.

Impact:

The findings directly inform Airbnb hosts on how to strategically enhance their listings to attract more positive reviews, thus improving their visibility and booking rates in a competitive market. These recommendations from the project are expected to significantly boost host revenue by optimizing listing attributes that are most influential in guest satisfaction.

Technologies Employed:

●     Data Analysis: Python, R

●     Data Visualization: Tableau

●     Statistical Modeling: Linear Regression, Correlation Analysis

●     Text Mining: Sentiment Analysis with BING and AFINN Lexicons