  Emotion detection from the text is an important and challenging problem in
text analytics. The opinion-mining experts are focusing on the development of
emotion detection applications as they have received considerable attention of
online community including users and business organization for collecting and
interpreting public emotions. However, most of the existing works on emotion
detection used less efficient machine learning classifiers with limited
datasets, resulting in performance degradation. To overcome this issue, this
work aims at the evaluation of the performance of different machine learning
classifiers on a benchmark emotion dataset. The experimental results show the
performance of different machine learning classifiers in terms of different
evaluation metrics like precision, recall ad f-measure. Finally, a classifier
with the best performance is recommended for the emotion classification.
