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This paper proposes a method to detect unexpected correlation from between a current topic and products word of mouth in buzz marketing sites, which will be part of a new approach to marketing analysis. For example, in 2009, the super-flu virus spawned significant effects on various product marketing domains around the globe. In buzz marketing sites, there had been a lot of word of mouth about the "flu." We could easily expect an "air purifier" to be correlated to the "flu" and air purifiers' shipments had grown according to the epidemic of flu. On the other hand, the relatedness between the "flu" and a "camera" could not be easily expected. However, in Japan, consumers' unforeseen behavior like the reluctance to buy digital cameras because of cancellations of a trip, a PE festival or other events caused by the epidemic of flu had appeared, and a strong correlation between the "flu" and "camera" had been found. Detecting these unforeseen consumers' behavior is significant for today's marketing analysis. In order to detect such unexpected relations, this paper applies the dynamic time warping techniques. Our proposed method computes time series correlations between a current topic and unspecified products from word of mouth of buzz marketing sites, and finds product candidates which have unexpected correlation with a current topic. To evaluate the effectiveness of the method, the experimental results for the current topic ("flu") and products ("air purifier", "camera", "car", etc.) are shown as well. By detecting unexpected relatedness from buzz marketing sites, unforeseen consumer behaviors can be further analyzed.