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As the ubiquitous communication environment advances, there is a demand for services that deliver the type of content appropriate for the preferences of the user. Services are already available that identify the user's preferences by analyzing his/her log information, and recommend items of content suitable for him/her. Methods of inferring the user's mood by partially analyzing pictograms frequently used in his/her emails have been proposed. In this paper, we extend such methods in order to enhance the accuracy of mood inference. Specifically, we propose a method of inferring the user's mood from emoticons, a set letters to form a face that graphically expresses the user's emotion, used in his/her multiple emails, and a method of identifying the user's preferences by analyzing pictograms that are used in his/her single email. We extract mood elements and preference elements and their intensities from pictograms and emoticons, and register them in respective databases. We developed a prototype system for evaluation. The study of the sample emails collected using this system confirmed the effectiveness of the proposed methods.