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The growing number of twitter users create large amounts of messages that contain valuable information for market research. These messages, called tweets, which are short, contain twitter-specific writing styles and are often idiosyncratic give rise to a vocabulary mismatch with typically chosen keywords for tweet collection. We propose a method that uses a new form of query expansion that generates pairs of search terms and takes into consideration the language usage of twitter to access user data that would otherwise be missed. Supervised classification is used to maintain precision by comparing collected tweets with external sources. Evaluation was carried out by collecting tweets about five different television shows during their time of airing and indicate, on average a 66.5% increase in the number of tweets compared with using the title of the show as the search terms and 68.0% total precision. Classification gives an average increase of 55.2% in number of tweets and 82.0% total precision. The utility of an automatic system for tracking topics that can find additional keywords is demonstrated.