IDENTIFICATION OF BEST PLACES FOR TOURISM BY USING SENTIMENT ANALYSIS

Abstract

Technological developments have radically changed how all players involved in tourism create and ingest knowledge. Tourists will also browse multiple souices of knowledge to generate their own information and share their insights and impressions with them. Tourism data generated by social media has in many ways become a very valuable source of information that impacts tourism. The amount of information available on the internet has now reached a degree that makes it nearly difficult to manage manual processing, demanding new theoretical approaches. Analysis of sentiment is increasingly evolving in reviews as an automated method of analysing semantic relationships and context. Our results indicate that the combined NB, SVM, KNN and Decision Tree characteristics are favourable to improved outcomes