Dynamic Mood Detection in Chat Application Using Text Pattern Analysis

Abstract

In modern communication and social networking the peoples of different ages are of different moods while chatting. This paper deals with detecting the modes and identifying human emotions through text mining. This paper explored to detect the mood variation of different age group that swings is maximum as compared to other age group. The moods can be classified in the basis of gender, age group and the emotion while texting. The random sample is taken from public chat in that the users are manually classified for strength of positive and negative emotions. By classifying emotions and using decision tree different variations are analyzed in this paper. Outlier study is used to recognize emotion distinction in child having any kind of disability. The pattern of the text is analysed and clustered and with the help of Besiyan classifier the text is classified in accordance with their emotions.