Case Study

Social Media Sentiment Analysis

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Challenge

A Fortune 500 e-commerce company needed a data pipeline to ingest its Twitter stream into real-time, after which tweets would be cleansed and transformed for the purpose of conducting sentiment analysis on marketing campaigns. Its legacy pipeline would not suffice as it proved to be too slow and costly.

Using Cask Hydrator, the company’s in-house team of Java developers built a real-time pipeline in two weeks using the drag-and-drop visual interface in Cask Hydrator. They developed a sentiment analysis transform using the API and included it in the pipeline. Lastly, they added multidimensional aggregations without having to write code, using CDAP’s Cube Plugin as a sink. This accelerated the time to value even further and enabled the company to conduct brand sentiment analysis by monitoring Twitter in real-time.

Benefits of Cask Solution

Rapid Time to Value
  • In-house Java developers were able to build the pipeline and sentiment analysis plugin with only a four-hour learning curve.
  • The new pipeline cleansed, transformed, analyzed and aggregated tweets at the rate of the full Twitter firehose in real-time.
Cost Savings and Business Improvements
  • The new pipeline produced quicker results, enabling faster and better decision making while cutting costs.
  • The analysis of Tweets in real-time allowed the business to make faster decisions on their campaigns.
  • Seamless transparency through custom dashboards provided easy operational insights and aggregated logs for debugging.
  • The infrastructure was consolidated into a single Hadoop cluster.
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