@inproceedings{deshmane-friedrichs-2017-tsa,
    title = "{TSA}-{INF} at {S}em{E}val-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for {T}witter Sentiment Analysis",
    author = "Deshmane, Amit Ajit  and
      Friedrichs, Jasper",
    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/fix-sig-urls/S17-2135/",
    doi = "10.18653/v1/S17-2135",
    pages = "802--806",
    abstract = "This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3{\%}. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations."
}