Social Media Coverage at Text Analytics Summit
17 June 2008 |
Today’s 2008 Text Analytics Summit involved several good sessions on the analysis of social media data. In keeping with the event’s enterprise software focus, these discussions emphasized the challenges organizations face in mining social media data to accomplish marketing objectives, creating and improving customer relationships being primary among them. |
- Matthew Hurst provided interesting big-picture perspective on the current and future states of social media analysis based on his research experience at Microsoft Live Labs. I really appreciated his breakdown of the present state into two realms:
- Content - techniques that analyze the content of a document (e.g. entities, sentiment, topics, etc.)
- Structure - techniques that analyze the network context of a document (e.g. links, threads, etc.)
He then described an overall process built around these two core pieces, including mention of a third realm of temporal analysis - it would be nice to get more explanation on this and on the whole present state picture as Matthew didn’t have a chance to go into much detail. I really liked that he took a complex situation and explained it in straightforward way - one of those moments when someone describes something, about which you’re already familiar, and without introducing a lot of new information, helps you to gain valuable insight.
His response to my question about where tagging fit into his model was insightful, though I don’t think I’m yet fully convinced about how applicable the first statement is to many applications of tagging. He said that they have generally found tags to be of little value when applied to documents because the information in the tags is often redundant with respect to the information in the text. He did mention that that tags are highly valuable when applied to other media types like images (e.g. Flickr’s very successful tagging system) - right on the money.
Matthew was good enough to post his slides along with a recap.
- Though not related to social media specifically, definitely deserving mention was Justin Langseth’s 1-hour demo of a Clarabridge implementation. Not to be confused with an end-user demo, this was a live walk-through of the entire process of deploying and then using their text analytics platform against a modest customer feedback dataset, in one hour. Clarabridge technology, and Justin’s technical presentation of it, are pretty impressive. These guys understand BI, and seem to have figured out a good way to structure text data such that it works well within the major enterprise BI frameworks, along with the necessary infrastructure and admin tools to hook everything up without a lot of pain. In my opinion, their current Windows orientation is less than desirable from a technology perspective. But their platform decisions, like the use of J2EE, suggest that they appreciate the importance of portability long-term. And if the enthused customers in attendance are any indication, the business value of what they’re doing far trumps this concern.
- Andrew Bernstein, of Cymfony, related insight from his work in helping companies with large consumer brands monitor market sentiment, test-market new products, and implement market intelligence using data sourced from MySpace, Facebook, blogs, etc. Interestingly, his four-quadrant social media matrix recommends that companies focus their analytics efforts on social networks first with blogs a close second in terms of market intelligence value. Social bookmarking, perhaps not surprisingly, faired poorly on this list. Much more surprisingly given its heavy overlap with both blogging and social networking, Twitter ranked in last place as a valuable source of market intelligence.
on August 9th, 2008 at 11:54 am
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