@inproceedings{s-etal-2017-ssn-mlrg1,
title = "{SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel {G}aussian Process Regression Model",
author = "S, Angel Deborah and
Rajendram, S Milton and
Mirnalinee, T T",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2139",
doi = "10.18653/v1/S17-2139",
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.",
}
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%0 Conference Proceedings
%T SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
%A S, Angel Deborah
%A Rajendram, S. Milton
%A Mirnalinee, T. T.
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F s-etal-2017-ssn-mlrg1
%X 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.
%R 10.18653/v1/S17-2139
%U https://aclanthology.org/S17-2139
%U https://doi.org/10.18653/v1/S17-2139
%P 823-826
Markdown (Informal)
[SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model](https://aclanthology.org/S17-2139) (S et al., SemEval 2017)
ACL