This repository was created as a part of the AI Talent Hackathon. During this hackathon, the problem of analyzing reviews of X5 retail stores was solving. As a solution, we have proposed the GeoPlatform - a tool for analyzing reviews both for one store and for a set of stores in a district/city.
The data was collected from online map services such as Yandex Maps and 2GIS. The result of the data collection was more than 5,000 reviews on 55 stores of 5 different retailers. All the collected data as well as the python scripts for it collection can be find in the data-parsing root.
In the ML part of the project we faced with hierarchical text classification by store parameter and it's sentiment.
The first-level model classifies the key storage parameter. The second one - sentiment of this parameter. Due to the missing markup, the zero-shot text classification approach was chosen.
Two models from Hugging Face were considered:
- zero-shot-classify-SSTuning-XLMR, cause it's fast and easy to integrate;
- multilingual-e5-large-xnli-english, cause it's powerful.
Python scripts with the model and the results are in model root.
For result demonstration were used:
- pydeck layers for map building and points plotting;
- streamlit for app deploying.
Try our app here: https://geoplatforma.streamlit.app