Event Stream Processing with Text Analytics

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Is text analytics part of your current analytical framework?

For many SAS customers, the answer is yes, and they've uncovered significant value as a result.

As text data continues to explode both in volume and the rate at which it's being generated, SAS Event Stream Processing can be used to analyze not only high-velocity structured data, but also the text (by using text models in stream).

In some cases, standard batch processing delivers the analytical insight sufficient for organizations. Yet, what about those other situations where taking action immediately, as an event is happening, is critical? These sub-second actions and real-time alerts can save or make millions of dollars for a company.

Below, I describe techniques that highlight streaming analysis of text data (and many of these elements also apply to structured data as well). My hope is this will trigger ideas and use cases for you to think about within your company.

1.)   Data Quality and Cleansing

Anyone who has worked with social data (or any text data for that matter) understands that it can be cluttered with noise, encoding issues, abbreviations, misspellings, etc. If not corrected, this can lead to inaccurate results and even processing errors. So why not deploy Event Stream Processing to correct and transform variables before they hit your database? As you’d expect, not every data quality issue can be resolved on the frontend of data collection, but by applying known corrections upfront, you have the ability to enrich your data and enhance the value of data sitting within your database.

Image 1: Diagram of an Event Stream Processing flow, integrating text analytics, pattern detection, and predictive modeling.

Image 1: Diagram of an Event Stream Processing flow, integrating text analytics, pattern detection, and predictive modeling.

2.)   In-Stream Sentiment Analysis and Categorization

SAS has a powerful set of text analytics technologies that customers have been using for over 10 years. In the latest release of SAS Event Stream Processing (version 3.1, which comes out in May), customers who currently license SAS Sentiment Analysis, SAS Content Categorization, or SAS Contextual Analysis can now deploy these models against streaming data. This opens a window of opportunity to tag unstructured data on the fly (such as sentiment scoring, classifying documents, or extracting entities). These results are then inputs to event stream models for additional scoring, or to generate alerts, prompts, or to take a specific action. To learn more about SAS Text Analytics, check out SAS Contextual Analysis, SAS Text Miner, and SAS Sentiment Analysis.

3.)   Embedded Modeling

In text analytics, the goal is to convert unstructured data into some structured format, such as flags, scores, categories, and entities. For many applications, these new variables are most valuable when they are used to enhance predictive models, trigger alerts, create risk scores, enrich content, and to ultimately track and report. Through embedded analytics, SAS DS2 code (and functions in C++, XML, and regular expressions may also be used) can be deployed within event stream processing flows, which means real-time scoring of both structured and unstructured text using regression models, decision trees, and more.

4.)   Integrated Data Sources

In many situations, insights from streaming data can only be realized when multiple data streams are integrated together. SAS Event Stream Processing allows users to join and merge data in stream, so that the calculations and models may be applied to the comprehensive dataset. For example, a large call center has streaming data in the form of customer complaints and service-

Image 2: SAS Event Stream Processing Streamviewer

Image 2: SAS Event Stream Processing Streamviewer

related questions. Once a customer comment is received, SAS Event Stream Processing can extract the customer name and/or customer ID and match it to transactional history for that customer, while also categorizing the reason(s) of the complaint or question. This in turn can trigger a prompt to the agent to adopt a retention strategy or potentially upsell them to a new product or service.

5.)   Emerging Issue Detection

As data floods into you organization, it is sometimes difficult to spot emerging trends and issues. Currently, many organizations run batch jobs to detect and resolve these issues. Because SAS Event Stream Processing can be both stateful and stateless, aggregations and advanced models can be used to identify emerging topics, categories, sentiment and other indicators in real-time. These emerging issues can be detected using sophisticated pattern matching that supports detecting patterns based on the relationship of one event to one or more other events within a defined period of time. Thresholds can be set and events can be used to determine the relevancy and immediacy of any associated instruction/action. This changes the process from being reactive to proactive in the sense that an emerging issue can be monitored in real-time.

Real-time systems such as SAS Event Stream Processing are used for a variety of purposes. By integrating this technology as a front end to key, time-sensitive deployments, organizations gain a competitive advantage in both time and quality.

To learn more about SAS Event Stream Processing, check out the following links for more information and feel free to contact us if you'd like more information.

Also, if you're out in Dallas at SAS Global Forum next week, be sure to stop by and check out SAS Event Stream Processing.

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About Author

Dan Zaratsian

Sr. Solutions Architect

Dan Zaratsian is a Sr. Solutions Architect with SAS' Global Analytics Practice, specializing in real-time event stream processing, text analytics, and machine learning. He works with a variety of technologies, both open-source and enterprise software, in order to design, program, and implement analytical solutions for clients. Dan holds a M.S. in Analytics from North Carolina State's Institute for Advanced Analytics and a Bachelor’s degree in Electrical Engineering from the University of Akron.

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