Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics
This work addresses decision-making for tourism businesses by providing automated analytics from social media, but it is incremental as it applies existing machine learning techniques to a specific domain.
The authors tackled the challenge of manually analyzing large volumes of social media data in tourism by proposing a platform that uses machine learning to identify discussion pathways, aspects, sentiments, and emotions, with a visualization tool to present insights for decision-making.
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.