Monitoring your brand: Sentiment analysis

Earlier this year at SAP’s Business Suite 7 launch I saw a demo of some text analytics capability developed by Business Objects for sentiment analysis used as a kind of an online sentiment monitor feeding into SAP CRM. Interesting concept and one that is increasingly compelling for businesses as more and more real time social content is created. There are a couple of key bits of technology at play here: real time content creation, real time search (text mining) and some linguistic analysis algorithm. Beyond these basics it comes down to what do you do with the analysis once it is located, captured and run through the language sausage grinder.

The information available about your brand on the real time web falls into two basic buckets. On one side there’s the immediate customer issue / problem / question. I’ve talked about this customer service opportunity, the delivering great service “when, where and how” the customer wants it and plan to make the topic a part of a larger post on social customer service in the near future.

Today I’m talking about a broader category that is your real time social web brand image and opinion (or sentiment). The customer service …

Earlier this year at SAP’s Business Suite 7 launch I saw a demo of some text analytics capability developed by Business Objects for sentiment analysis used as a kind of an online sentiment monitor feeding into SAP CRM. Interesting concept and one that is increasingly compelling for businesses as more and more real time social content is created. There are a couple of key bits of technology at play here: real time content creation, real time search (text mining) and some linguistic analysis algorithm. Beyond these basics it comes down to what do you do with the analysis once it is located, captured and run through the language sausage grinder.

The information available about your brand on the real time web falls into two basic buckets. On one side there’s the immediate customer issue / problem / question. I’ve talked about this customer service opportunity, the delivering great service “when, where and how” the customer wants it and plan to make the topic a part of a larger post on social customer service in the near future.

Today I’m talking about a broader category that is your real time social web brand image and opinion (or sentiment). The customer service information is specific and individual, social brand sentiment is an aggregation of social data from across the real time web. From this aggregated data it is possible to gauge overall public opinion about your brand at any given time (assuming you can collect that data of course). The data source is any or all online forum where people gather and discuss their opinions from public sources like Twitter and Facebook to your own private label communities. Once collected the data can be analyzed as text and using linguistics sentiment trends can be established from the tone of the language and the type of words used to describe feelings and opinions. The more data and data sources the more likely that the sentiment represented accurately reflects online opinion trends.

OK, cool technology, but why should you care? Think about brand and how, in the past, in an offline world you would have tried to capture public opinion about your brand. First of all it was very expensive using surveys or focus groups. Secondly it was very time consuming and the data could easily be out of date by the time you had completed the analysis. This method is really historical brand sentiment at best. Today, using real time web, you could know with reasonable accuracy what the online world (a world this is rapidly growing all the time) thinks about your brand with reasonable accuracy. As a marketer that’s a powerful tool. The uses span from crisis monitoring to marketing messaging (and method) testing.

Tools to enable sentiment monitoring and analysis are starting to emerge from both existing CRM vendors and also start ups with new product offerings. I mentioned the SAP / Business Objects capability; while not a full product the text analytics engine, it does have the capability to do sentiment analysis. Web analytics vendor Omniture offers Twitter integration to its SyteCatalyst analytics platform that allow sentiment monitoring. It does not, however have a linguistic analysis capability but instead lets you search keywords and combinations of keywords and slice and dice those searches. Sysomos offers broader social web analytics (2 tools, Map and Heartbeat) by searching blogs, wiki’s, and social sites like Twitter.

Once the data is collected it can be analyzed in a variety of ways (but no text analysis / linguistics capability). Evri, a Web 3.0 Semantic search engine, has a sentiment web API that pulls search data into it’s semantic engine to provide sentiment measurement. The tool is basically binary, measuring positive or negative sentiment. SPSS has rich text mining capabilities in its PASW Modeler 13 product that allows deep analysis of text mining results.

Autonomy, Nielsen and Techrigy all have sentiment analysis capabilities as well.