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This paper utilizes an attention concept approach in text mining to address the deficiencies of existing keyword search engines. We show how an attention concept in conjunction with a traditional search approach can be used to develop an adaptive text mining model with user-oriented, time-based and attentive knowledge. Without changing a user's search behavior, this paper considers some specific post-search operations as attentive targets for building the personalized interest base. This interest base is further shown on an interest map via the self-organizing map algorithm (SOM). By comparing the personalized interest map, the original search results from a keyword search engine are re-ranked. Experimental results demonstrate that the attentive search mechanism is able to improve user satisfaction.