Social Media Monitoring Of Airbnb Reviews Using AI: A Sentiment Analysis Approach For Immigrant Perspectives In The UK

Abstract

This paper presents a novel approach for monitoring social media content related to Airbnb reviews, explicitly focusing on the sentiments expressed by immigrants in the United Kingdom. The proposed system, a Quick Search System, leverages machine learning techniques to perform sentiment analysis on many Airbnb reviews. The system aims to provide timely and insightful information about the experiences and sentiments of immigrants in the UK, as reflected in their Airbnb reviews. By employing state-of-the-art machine learning algorithms, the system enables efficient and accurate sentiment classification, allowing for the identification of key themes and sentiments expressed by immigrant users. The study demonstrates the potential of this approach in gaining a deeper understanding of immigrant perspectives within the context of peer-to-peer accommodation, and its implications for social media monitoring and customer satisfaction management.This present study has conducted a critical analysis utilizing efficient feature extraction techniques, including N-grams and TF-IDF, to optimize identifying positive, neutral, and negative feedback. Furthermore, five different models were utilized, and the training and testing processes were accompanied by parameter tuning. Ultimately, the study concluded that the Random Forest (RF) classifier performed exceptionally well, achieving a 95% accuracy rate.