I was following the Text Analytics Summit #TAS13 online over the last few days and it got me thinking about content analytics (which is where my interest in analytics started more than a decade ago) and the role of social & semantic graphs. I’ll be posting some thoughts on this next week, but this older post is on topic so I thought I would reshare.
Originally posted on Marie's Ramblings & Ruminations:
In the real world, when we think about sentiment we think about a person and their opinion. Joe hates X, while Jane loves it, and Frank is on the fence. When we want to reward our best customers (our brand advocates) or convert our detractors we again think about Joe, Jane and Frank. Yet in the virtual world of social media, the software programs that try to identify sentiment frequently only think about the content. The content is negative or the content is positive as though the content had a mind of its own. Actually if we really think about it, its kind of absurd. Now I appreciate that incorporating social analytics into the equation is non-trivial, however it’s hard to imagine us making any headway at all vis-a-vis social media engagement until we put social analytics (the people) up front and center. Some of the interesting challenges are:
Social ID Disambiguation
Now I mention this first because it is clearly one of the suckiest problems we have to solve in building social profiles, and while I am not an expert in this space I am glad to say that we have loads of people out there who are. Historically organizations from the intelligence sector to the gaming industry have been using a variety of techniques, such as IBM’s Identity Insights, to find patterns in data and use those patterns to disambiguate people, such as connecting aliases and the likes. Sound familiar?
Translating Content Sentiment into Personal Opinion
Just because Joe happens to say something negative about X doesn’t make him a detractor. However if it comes from someone who is normally positive about your brand (maybe an existing customer), then even a single comment could be important. Similarly a positive comment from a brand detractor is potentially an opportunity to convert someone from negative to positive (or at least neutral). So how do we take the aggregate of sentiment signals a person leaves out in the socialsphere, and in combination with other features, such as affiliations, translate this into an opinion classification?