Ten years ago all complaints to a company came through phone calls. Nowadays all complaints come through social media. This is a revolutionary change, and companies invest a lot of resources on attending these channels. Increasing amount of people communicate e.g. for water service issues through social media and the identification of incidents among the massive amount of messages before they escalate represented a challenge for the operators.
Using a series of data science tools as clustering algorithm, a trained neural network for Name Entity Recognition (NER), and a map matching tool to create a geographic and temporal map of all ‘incident specific’ posts, Natalia built an early warning system that allows the company to live-localize incidents, detect the entity of it, and manage efficiently social media complaints using as source of information Twitters.
Natalia Grion completed her PhD in cognitive neuroscience in 2011. She then worked as a scientist at University of Leicester (UK) and SISSA (Italy), consolidating her experience in building up multidisciplinary scientific projects. After her research life she started the data scientist career path working at a Tech company in London. In October 2019 she joined the BI Team of ProntoPro-Milan, a fast growing marketplace company. There she develops machine learning business solutions for the different departments.
(Image / Video (c) Thomas van Emmerik)