@inproceedings{s-etal-2017-ssn,
    title = "{SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter Using Multi-Kernel {G}aussian Process Classifier",
    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://preview.aclanthology.org/iwcs-25-ingestion/S17-2118/",
    doi = "10.18653/v1/S17-2118",
    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."
}