Blogs and tweets posted by consumers on social media sites provide a wealth of information for sentiment analysis; however, this data is not structured. Consumers do not always use proper company or product names. The data contains a fair amount of slang words, and there is a mix of languages in a multiethnic, multilanguage environment. They may use a variety of words to convey positive or negative sentiments. The link to the author is not very well articulated.

We start with scant information, such as Twitter handles and unstructured references, and filter and link this data to decipher demographics, location, and other important characteristics required in making this data meaningful to a marketer.

How do we now link the Twitter handle to the customer ID? Obviously, the customer would be the best person to link them together, and customers can sometimes be incentivized to do so with product promotions or information exchange. When such direct means are not available, entity resolution technologies are providing ways to discover and resolve identities.

Identity resolution is the next step in the evolution of matching technologies for MDM. Initially developed by Jeff Jonas for the casino industry, this is a powerful technology that takes into account both normal as well as deceptive data from customers. The technology is based on a set of rules that places the probability of a match on a set of seemingly unrelated facts. As hard facts match, the probabilities are altered to reflect newly discovered information. Customer-initiated actions, such as accepting a promotion, can be hard evidence added to customer handles or user IDs, connecting them to device IDs, product IDs, or customer account information.

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