Review2topic: Building Topics Detection Model to Leverage Reviews Data in

Having billions of customers reviews, we would like to better understand them and leverage this data for different use cases. For example, finding popular activities per destination, detecting popular facilities per property, allowing the users to filter reviews by specific topics, and detecting violence in reviews.

In this talk, we will present how we build a multilingual multi-label topic classification model that supports zero-shot, to match reviews to unseen users’ search topics.
We will show how fine-tuning BERT-like models on the tourism domain with a small dataset, can outperform other pre-trained models.

About the speaker

Moran Beladev

Machine Learning Manager at

Machine learning manager in, researching computer vision and NLP domains. Doing a Ph.D in information systems engineering in Ben Gurion University, researching NLP aspects in temporal graphs.
Previously worked as a Data Science Team Leader at Diagnostic Robotics, building ML solutions for the medical domain and NLP algorithms to extract clinical entities from medical visit summaries.



Sessions: October 4 – 6
Trainings: October 11 – 14


Presented by