Customer Story

Real-time multi-lingual sentiment analysis for a top U.S. telecom provider

Challenges

A major telecom company providing nationwide telecom services wanted a system that performs real-time, multi-lingual classification and sentiment analysis of text data. The client was looking for a solution that allows storing, indexing, and querying Petabytes (PBs) of data with a very high throughput. Some of the critical requirements were:

  • Ingest and parse high volume of data [250M (15 TB) records/day] of varied types (for example, weblogs, email, chat, and files)
  • Apply real-time multi-lingual classification and sentiment analysis with very high accuracy (four nines)
  • Store metadata and raw binary data for querying
  • Query SLA – 5s on cold data

Solution

The solution provided by Impetus had three modules:

  • Analytics Module: Responsible for performing text categorization and sentiment analysis. It implements a matrix decomposition-based text-classification algorithm. The incoming test document had to pass through a series of pre-processing and numerical computations. Impetus designed the classifier to achieve very low latency.
  • Event Store/ Indexer Abstraction Layer: Responsible for storing and indexing the information based on the configuration
  • Publish Module: Responsible for publishing the analytical result or event data to the external system

Results

  • Rapid and accurate real-time text categorization and sentiment analysis
  • Adjustable text categorization for domain-specific classes
  • Multi-lingual support
  • Enhanced sentiment analysis to focus on feature-specific opinion mining
  • Linear scalability to increase the number of nodes in the cluster
  • Provision to add custom component for added functionalities

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