Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics

TL;DR

This paper presents a social media analytics platform that leverages machine learning to extract sentiment and emotional insights from tourism-related social media data, aiding decision-making in the tourism industry.

Contribution

It introduces a novel platform that automatically identifies discussion pathways, sentiments, and emotions from social media data to support tourism decision-making.

Findings

Effective identification of discussion pathways and key topics

Sentiment and emotion analysis provides valuable feedback

Visualization enhances understanding of social media insights

Abstract

Social media has gained an immense popularity over the last decade. People tend to express opinions about their daily encounters on social media freely. These daily encounters include the places they traveled, hotels or restaurants they have tried and aspects related to tourism in general. Since people usually express their true experiences on social media, the expressed opinions contain valuable information that can be used to generate business value and aid in decision-making processes. Due to the large volume of data, it is not a feasible task to manually go through each and every item and extract the information. Hence, we propose a social media analytics platform which has the capability to identify discussion pathways and aspects with their corresponding sentiment and deeper emotions using machine learning techniques and a visualization tool which shows the extracted insights in a comprehensible and concise manner. Identified topic pathways and aspects will give a decision maker some insight into what are the most discussed topics about the entity whereas associated sentiments and emotions will help to identify the feedback.