@InProceedings{s-rajendram-mirnalinee:2017:SemEval2,
  author    = {S, Angel Deborah  and  Rajendram, S Milton  and  Mirnalinee, T T},
  title     = {SSN\_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model},
  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     = {823--826},
  abstract  = {The system developed by the SSN\_MLRG1 team for Semeval-2017 task 5 on
	fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for
	identifying the optimistic and pessimistic sentiments associated with companies
	and stocks. Since the comments made at different times about the same companies
	and stocks may display different emotions, their properties such as smoothness
	and periodicity may vary. Our experiments show that while single kernel
	Gaussian Process can learn certain properties well, Multiple Kernel Gaussian
	Process are effective in learning the presence of different properties
	simultaneously.},
  url       = {http://www.aclweb.org/anthology/S17-2139}
}