Data Mining Helps Businesses Locate Their Customers’ Tweets

Can an organization track complaints using Twitter? You might be surprised but a surprising amount of information can be unearthed by mining certain phrases like “I can’t get my widget to work” or “Does anyone has the same problem as me with …?”

By aggregating mass amounts of Tweets, organizations can now tap into what is really happening in the marketplace. The best part is you don’t have to wait months and months for detailed studies. Companies today can get the information they need very rapidly on trends thanks to some new, rather sophisticated, research and analysis.

The field of Social Web Search and Mining (SWSM) has come alive as companies realize they can tap into what is actually happening — not just what their “gut feeling” tells them. This year, the Social Web Search and Mining Workshop gave its Best Paper award to AT&T Labs’ Researcher Narendra Gupta for work done on a paper entitled, Extracting Descriptions of Problems with Product and Services from Twitter Data.

In this work, you’ll read about what they did to measure effectiveness using the F-measure (a statistical tool used in research to determine accuracy) with Twitter data. The research used automatic extraction of descriptions of problems from Twitter data to determine results.

What is most amazing is how they draw valid implications from Twitter given the nature of communication used such as emoticons (e.g. : -), :-0 and other symbols), a variety of abbreviations which are used (e.g. ROFL, IMHO, etc.) and other unique terms (hash tags, RT, @mentions, etc.). By categorizing, then compiling, and later analyzing a massive amount of data, trends can be spotted long before they are traditionally available with most old-school reporting tools.

This breakthrough study is worth reading if you are concerned about what customers think about your products and services. Find out what trends are emerging before others know. To find out more about this exciting new research, read this award-winning White Paper at Extracting Descriptions of Problems with Product and Services from Twitter Data.

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