Sentiment Search tracks and analyses social media activity for every restaurant and hotel in any given city.
Millions of reviews, tweets and blogposts are posted daily. This wealth of information sits on the web and would help generate deep insights about any given hotel or restaurant, their competitors and the industry as a whole. Existing analytic solutions however, are unable to harness this information, on two counts: 1) No solution is specialised for hospitality, making detail and quality of insights primitive; 2) No solution tracks every restaurant and hotel in any city and therefore cannot deliver a holistic view of the industry. All this results in restaurants and hotels that are unable to make in-depth comparisons with competitors, identify new ones, and detect developing trends across their own industry. Most importantly, they are unable to pinpoint exactly which areas of their business that require attention. The volume of information on the internet is fragmented and overwhelming which makes it impossible to analyse manually.
Using AI algorithms Sentiment Search puts forward a solution that solves exactly this challenge: it aggregates and analyses reviews for individual restaurants and hotels in any city providing them with a comprehensive picture of consumer sentiment. The algorithms have been developed in-house by Sentiment Search to cater specifically to the hospitality industry, just like everything about the tool.
The idea first came about when buying a mobile phone on Amazon in 2011. With 100s of reviews for each phone, it became tedious to try to analyse it all.
This prompted the development of a tool that would analyse all reviews. Prithvi was curious, so extrapolated the tool to restaurants and hotels because of how useful it could be to those businesses. The company started out as a self-funded one person start-up that signed paying customers.