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This paper presents CAO, a system for affect analysis of emoticons in Japanese online communication. Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The presented system extracts emoticons from input and determines the specific emotion types they express with a three-step procedure. First, it matches the extracted emoticons to a predetermined raw emoticon database. The database contains over 10,000 emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing “mouths” or “eyes,” based on the idea of kinemes from the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is first based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides hints about potential groups of expressed emotions, giving the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.