Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Evaluation of Supervised Machine Learning Techniques for
  Efficient Detection of Emotions from Online Content   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.

30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn