An enhanced approach for classifying emotions using customized decision tree algorithm

This investigation reports the improved method for the text based emotion classification and prediction using a customized decision tree algorithm. Machine learning techniques such as Decision tree algorithm are widely used in research fields of bioinformatics, data mining, capturing knowledge in expert systems and so on. The emotions can be deducted from the online chat conversation and tagged. In this proposed work, the given dataset is classified using customized decision tree with respect to the two known classes of data. The main motivation behind this customized approach is to provide a simple, effective, less complex and memory optimized prediction model in deducing the classes of the given dataset. The effectiveness of the approach is then obtained by comparing it with the existing methodologies.