A Proposal for Personalized Travel Recommendation System Through Affective Analysis of User SNS Data

Abstract

As the digital landscape transforms the way individuals plan and embark on their travels, the need for personalized travel recommendations arises. This paper outlines a text-focused approach for the development of a personalized travel recommendation system, leveraging affective analysis of Google reviews and users Social Networking Service (SNS) data. The proposed system aims to provide user-centric recommendations by using the social footprint users leave on the internet. The paper details the data collection, preprocessing, database creation, and user preference matching methods to create a personalized text recommendation model.