How Researchers Are Using Restaurant Reviews And Data Analytics To Predict Health Risks

All of us rely on online restaurant reviews before we try a new restaurant. These reviews are written by normal people like us not just help people recognise the good and bad restaurants or dishes but also add a greater value. In fact, after taking help from machine learning, this data can also be used to predict health risks. Becausereportshave suggested that restaurants are the most common source of foodborne illness.

In this article, we will discuss one suchresearchwhere data analytics tools to solve this problem of health risks.

Inspections Using Online Reviewsby Jun Seok Kang and Polina Kuznetsova of Stony Brook University, and Michael Luca and Yejin Choi of Harvard Business School tries to find an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient.

There have been studies in the past which tried to touch this issue using data analysis. But they concentrated on specific problems like influenza or food-poisoning and that is why they had to pay attention to a very small set of words for the NLP algorithm to train.

But this research differs from the rest of the attempts in the following two ways:

(1) It accounts for all the words that people use in the online restaurant reviews. They also considered words that may not be directly related to hygiene but are relevant to a certain extent. By doing this, they had more data and a broader set of conclusions to make as an aftermath of the analysis.

(2) This is the first work to use online reviews for the context of improving public policy and suggesting an additional source of information for policymakers.

The data set consisted of restaurant reviews from 2006 to 2013. Reviews that had words which suggested an unlikable taste in the food like “raw” or “salty” were not chosen since the research involved health concerns, not taste. Reviews containing severe things affecting health were only considered because they are the ones that deserve more attention and are of importance to food inspectors.

The team used in total two methods to examine and predict this because one kind of classification alone was not enough to extract a meaningful enough prediction. So, they had two kinds of predictions added together — the first one based on hygiene reviews, and the second one on content.