Leverage Reviews Data for Multi Label Topics Classification in Booking.com


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, detecting violence in reviews and summarizing most discussed topics per property.

In this talk, we will present how we build a multilingual multi-label topic classification model that supports zero-shot, to match reviews with 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 and will share experiments results of different architectures.

Furthermore, we will present how we collected the data using an active learning approach and AWS Sagemaker ground truth tool, and we will show a short demo of the model with explainability using Streamlit.


Moran is a machine learning manager at booking.com, researching and developing computer vision and NLP models for the tourism domain. Moran is a Ph.D candidate in information systems engineering at 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.

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