The social networks help sharing user’s multidimensional content which includes text, image, audio and video at any time. Emoticons are helpful in precise content sharing as an alternative of text and need to be analyzed for sharing of the right content. The content being shared mostly reflects the behavioral characteristics of the users and imitates their emotions. Therefore, each emoticon needs to be mapped with standard emotions. The emoticons proposed by Unicode consortium are considered and mapped with nine basic emotions such as love, happiness, pity, furious, heroic, fearful, disgust, wonder and peace. A prediction model based on decision tree classifier is designed to classify user’s contentaccording to the emotions expressed through the emoticons, especially for tweets. The designed methodology is demonstrated using two thousand tweets. Tweets are adopted for its simplicity and limited processing with only hundred and forty characters. The outcome obtained by applying the designed methodology provided satisfactory results of 83% accuracy which is more than the average accuracy (75%) of standard machine learning classification process. Therefore, it is possible to guess the behavior of the users through sharing the different forms of emoticons at various instances. This classification of users’ content would reflect the dominant emotions possessed by them. This finding helps in understanding the basic nature of an individual in social networks. Having identified the basic nature of an individual through emoticons, it is very easy to personalize the user’s social network page to filter disinterested and disgusting content at any time.