Argus Data Insights
By Vega IT
Client
Services
Project Description
Machine learning, business understanding: how we used NLP to optimize Argus media insightsArgus Data Insights uses a mix of technology and human input to monitor print, social media, TV and radio – giving detailed, actionable insights into the industry.The challenge: finding the most relevant insights for clientsThe client wanted to improve the accuracy, relevance and speed of delivery for their insights product. To do that, they wanted a software partner to use the latest data science models to create an advanced solution for the future.Results: An NLP-fuelled engine for detailed reports and rich insightsWe used multiple natural language processing (NLP) models to dramatically improve the quality of the media insights Argus could offer its clients. We focused on four areas to improve:Sentiment analysis Named entity recognitionTarget dominance Language detection Using language-agnostic NLPAt every stage of the project, we worked closely with the team at Argus to iterate towards the best result. Microservices to improve speedWe built every NLP model as an independent microservice with its own API – which makes them much faster to use.Working in German for multilingual solutionsArgus works primarily in German, so for this project we did too. It meant diving deep into the technical language of the space, and helped us work closely with Argus to deliver the best result. Flexible compatibility and increased speedWe provided an API for each NLP model, so it could be used independently by any of the client’s products. In combination with Elasticsearch, this solution significantly reduces the time for generating final results.The tech we used for this project:Standard Python for cleaning and tidying data NLP models from Hugging Face JupyterHub for finetuning Different models for different tasks Other tech:FastAPI library Elasticsearch React DevOps technologiesRead more: https://www.vegait.rs/media-center/business-insights/case-study-argus-data-insights/
Machine learning, business understanding: how we used NLP to optimize Argus media insightsArgus Data Insights uses a mix of technology and human input to monitor print, social media, TV and radio – giving detailed, actionable insights into the industry.The challenge: finding the most relevant insights for clientsThe client wanted to improve the accuracy, relevance and speed of delivery for their insights product. To do that, they wanted a software partner to use the latest data science models to create an advanced solution for the future.Results: An NLP-fuelled engine for detailed reports and rich insightsWe used multiple natural language processing (NLP) models to dramatically improve the quality of the media insights Argus could offer its clients. We focused on four areas to improve:Sentiment analysis Named entity recognitionTarget dominance Language detection Using language-agnostic NLPAt every stage of the project, we worked closely with the team at Argus to iterate towards the best result. Microservices to improve speedWe built every NLP model as an independent microservice with its own API – which makes them much faster to use.Working in German for multilingual solutionsArgus works primarily in German, so for this project we did too. It meant diving deep into the technical language of the space, and helped us work closely with Argus to deliver the best result. Flexible compatibility and increased speedWe provided an API for each NLP model, so it could be used independently by any of the client’s products. In combination with Elasticsearch, this solution significantly reduces the time for generating final results.The tech we used for this project:Standard Python for cleaning and tidying data NLP models from Hugging Face JupyterHub for finetuning Different models for different tasks Other tech:FastAPI library Elasticsearch React DevOps technologiesRead more: https://www.vegait.rs/media-center/business-insights/case-study-argus-data-insights/
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