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8/29/2008
09:52 AM
Seth Grimes
Seth Grimes
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Learning about Text Analytics

I spend a lot of time on teaching materials on text analytics: articles, presentations, and courses. I've gotten positive feedback about my introductory materials, which I designed for practitioners (like myself) rather than for academics or researchers. There are great resources out there — technical papers and white papers, case studies, software, etc. — but you have to get the basics down first...

I spend a lot of time on teaching materials on text analytics: articles, presentations, and courses. I've gotten positive feedback about my introductory materials, which I designed for practitioners (like myself) rather than for academics or researchers. There are great resources out there — technical papers and white papers, case studies, software, etc. — but you have to get the basics down first.You might start with a pair of articles I wrote for the Business Intelligence Network that present a version of my Text Analytics for Dummies class in narrative form: Text Analytics Basics, Part 1 and Part 2.

I built that class out into a longer introductory course for the Data Warehousing Institute class that I recently taught, Text Analytics for BI/DW Practitioners. Normally I avoid wordy slides — I prefer to use graphical illustrations — but TDWI likes course materials that carry text that attendees can refer back to so these slides may be of some use.

The basics articles in turn provide a technical foundation for reading a report I published in July, Voice of the Customer: Text Analytics for the Responsive Enterprise. (My VoC report was sponsored by a trio of vendors, but this was a case of finding someone to pay for work I wanted to do rather than a case of work for hire. My work was editorially independent.) The report covers an emerging application area, mining diverse material such as survey verbatims (free-text responses), forum postings, blogs and news articles, e-mail, etc. to capture market opinions and respond to product and service issues that affect customer satisfaction and market sentiment. It presents findings from the small-sample survey on VoC text-analytics best practices that I conducted a few months back.

VoC is garnering a lot of attention as a text-analytics application, per my reporting on last June's Text Analytics Summit. I'll cite a related article published recently, Attensity CTO David Bean on 8 CRM Uses for Text Analytics, that I found particularly useful. And if you want to go deeper into the technology, check out the slides from Bing Liu's accessible Opinion Mining and Summarization – Sentiment Analytics tutorial.

Again, many excellent text-analytics resources are available. If you are new to the topic, or if you want to see explore text-analytics from perspective of a BI focused practitioner (me), I hope these materials provide a good starting point.I spend a lot of time on teaching materials on text analytics: articles, presentations, and courses. I've gotten positive feedback about my introductory materials, which I designed for practitioners (like myself) rather than for academics or researchers. There are great resources out there — technical papers and white papers, case studies, software, etc. — but you have to get the basics down first...

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