Social media and news trends monitoring

Social media and news trends monitoring

The Problem

Professionals are looking for social media and news trends monitoring alternative data insights to gain a competitive advantage. Those data are hard to access, unstructured,  not easy to monitor.

Analysts spend as much as 35% of time just processing data, while only 20% is spent on interpreting the analytical results.

Our Solution

Event-driven, serverless cloud-based analytical pipelines. Each function takes a request from an analytical engine as an input and then fetches and processes relevant data. In data engineering pipelines we used Apache technologies such as Airflow and Beam. For NLP tasks transformer-based model BERT has been used which is state-of-the-art for Named Entity Recognition tasks.


Platform with active API for on demand/scheduled customer query - company, sector key-words. Ready to get actionable insights based on millions of articles, posts, comments from social media and news data, discover the undiscovered market anomalies and test hypotheses.

Key Takeaway

Analyst can spend up to 3x more time on interpreting social media and news trends.

Technology Keywords

Python, Apache, PostgreSQL, Cloud-based, NLP/BERT