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A Fortune 100 Energy Company identified a range of brand reputations drivers for the company, which now inform all communications and marketing activities. They required a natural language analysis engine to measure, correlate and visualize the drivers related to online and earned media and its potential effect on the company.
Vertical Marketing Optimization
Model Natural Language Processing
We developed a custom data platform to track the corporate reputation. This project included ingesting, correlating and aggregating all publicly available media sources on the open web related to the company or corresponding reputation drivers.
All data was scraped, ingested, and processed using a series of NLP techniques, including sentiment, topic modeling, classification, and grouping. For advanced processing analysis, data was persisted in a graph database for advanced in-pipeline analytics. The underlying data volume was very large, requiring in-memory processing for ongoing analysis.
We delivered a series of custom dashboards focused on topic modeling, time-series, anomaly detection and aggregation summaries in an interactive application. Due to the data volume, we used both large precomputation and near real-time aggregation indexing jobs to provide interactive dashboards.