  Detecting emotions from text is an extension of simple sentiment polarity
detection. Instead of considering only positive or negative sentiments,
emotions are conveyed using more tangible manner; thus, they can be expressed
as many shades of gray. This paper manifests the results of our experimentation
for fine-grained emotion analysis on Bangla text. We gathered and annotated a
text corpus consisting of user comments from several Facebook groups regarding
socio-economic and political issues, and we made efforts to extract the basic
emotions (sadness, happiness, disgust, surprise, fear, anger) conveyed through
these comments. Finally, we compared the results of the five most popular
classical machine learning techniques namely Naive Bayes, Decision Tree,
k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and K-Means Clustering
with several combinations of features. Our best model (SVM with a non-linear
radial-basis function (RBF) kernel) achieved an overall average accuracy score
of 52.98% and an F1 score (macro) of 0.3324
