Forex Tweet Graph Analytics with Neo4j
This project used graph modeling to analyze forex-related social media conversations. Instead of treating tweets as isolated text rows, the work modeled people, hashtags, market concepts, reports, events, and tradable instruments as connected entities.
Graph model
The concept map organized market-moving discussion into categories such as public figures, news, economic reports, assets, currency pairs, commodities, and hashtags. Neo4j made it possible to explore how those concepts connected and which nodes were most central in the conversation.
Analytics value
Graph analytics is useful when relationships matter as much as individual records. In this case, the relationship between market actors, event types, and instruments can reveal which ideas dominate the conversation and how attention moves through a trading topic.
Portfolio relevance
The project demonstrates social-media analytics, graph thinking, and concept modeling. It also connects naturally to current agentic AI research, where information flow, temporal context, and market narratives are important parts of decision evaluation.