@InProceedings{s-rajendram-mirnalinee:2017:SemEval1,
  author    = {S, Angel Deborah  and  Rajendram, S Milton  and  Mirnalinee, T T},
  title     = {SSN\_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {709--712},
  abstract  = {The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with
	bag of words feature vectors and fixed rule multi-kernel learning, for
	sentiment analysis of tweets. Since tweets on the same topic, made at different
	times, may exhibit different emotions, their properties such as smoothness and
	periodicity also vary with time. Our experiments show that, compared to single
	kernel, multiple kernels are effective in learning the simultaneous presence of
	multiple properties.},
  url       = {http://www.aclweb.org/anthology/S17-2118}
}