Case Study: Right Relevance achieves scalable social media influencer and sentiment analysis with Neo4j

Right Relevance - Customer Case Study

Right Relevance, led on the data side by John Swain (Product Manager, Data Science and Data Products), needed to analyze large-scale social media and influencer networks to monitor public sentiment around high-profile events like Brexit and the US presidential election. Coming from an SQL background, the team faced challenges modeling relationship‑centric data, running whole‑graph algorithms, and scaling to handle the volume of Twitter conversations.

They adopted Neo4j as the graph storage layer—extracting relationship data from MongoDB/Hadoop/SQL—and leveraged the APOC library to run whole‑graph algorithms (PageRank, betweenness centrality, community detection). With support from Neo4j’s developer relations on clustering and scaling, Right Relevance processed election‑scale data, published some libraries back to APOC, and achieved robust analysis of social conversations and public sentiment.

Right Relevance

John Swain

Product Manager