An emoticon is a metacommunicative pictorial representation of facial expressions, which serves to convey information about the sender's emotional state. To complement non-verbal communication, emoticons are frequently used in Chinese online social media, especially in discussions of health and fitness topics. However, limited research has been done to effectively analyze emoticons in a Chinese context. In this study, we developed an emoticon analysis system to extract emoticons from Chinese text and classify them into one of 7 affect categories. The system is based on a kinesics model which divides emoticons into semantic areas (eyes, mouths, etc.), with an improvement for adaption in the Chinese context. Empirical tests were conducted to evaluate the effectiveness of the proposed system in extracting and classifying emoticons, based on a corpus of more than one million sentences of Chinese health- and fitness-related online messages. Results showed the system to be effective in detecting and extracting emoticons from text, and in interpreting the emotion conveyed by emoticons.