Analysis and Indentifying Variation in Human Emotion Through Data Mining

This paper explored to detect emotion variation in adolescent age group through data mining techniques. In this age mood swings is maximum as compared to other age group. Children become more moody and irritable during this period of their lives .This moodiness is commonly attributes to the sudden and fluctuating hormonal level, brain still developing, peer pressure, Cognitive immaturity . Some people get more hyper but others feel under the weather and just want to be left alone, but everyone is different, and it isn't always the same emotion change. During this age period the people around us will experience drastic changes in our moods. If you're out-of-control happy one minute, totally depressed the next minute, and furious the next, you're completely normal!. But mood swings can also be frightening and confusing. Proposed paper is based on collection of dataset from this large group of emotions in adolescent age by using data mining technique. By classifying emotions and using decision tree different emotional variations are analyzed in this paper. Outlier analysis is used to identify emotion variation in child having any kind of disability. GeneralTerms:-emotion classification, adolescent age, classification decision tree, outlier analysis, data mining.