1. A computer-implemented method for generating two or more new ad campaigns from an original ad campaign, comprising: identifying an original ad campaign including two or more creatives, two or more negative key terms, and a group of positive key terms, each positive key term comprising a keyword or a key phrase; dividing the group of positive key terms associated with the original ad campaign into two or more sub-groups of positive key terms, at least one of the two or more sub-groups having a higher similarity level than the group of positive key terms; associating each sub-group of key terms with at least one of the two or more creatives, each association defining a new ad campaign; and for each negative key term of the original ad campaign: identifying a first set of topic clusters that are associated with the negative key term; identifying second sets of topic clusters that are each associated with one of the sub-groups of positive key terms; comparing the first set of topic cluster with each of the second set of topic clusters; determining, by a computer processor, which one of the two or more new ad campaigns is more relevant to the negative key term; and associating the negative key term with the new ad campaign that is more relevant.
1. A computer-implemented method for generating two or more new ad campaigns from an original ad campaign, comprising: identifying an original ad campaign including two or more creatives, two or more negative key terms, and a group of positive key terms, each positive key term comprising a keyword or a key phrase; dividing the group of positive key terms associated with the original ad campaign into two or more sub-groups of positive key terms, at least one of the two or more sub-groups having a higher similarity level than the group of positive key terms; associating each sub-group of key terms with at least one of the two or more creatives, each association defining a new ad campaign; and for each negative key term of the original ad campaign: identifying a first set of topic clusters that are associated with the negative key term; identifying second sets of topic clusters that are each associated with one of the sub-groups of positive key terms; comparing the first set of topic cluster with each of the second set of topic clusters; determining, by a computer processor, which one of the two or more new ad campaigns is more relevant to the negative key term; and associating the negative key term with the new ad campaign that is more relevant. 3. The method of claim 1 wherein at least the first set of topic clusters or the second sets of topic clusters are identified using a semantic database.