@inproceedings{ayata-etal-2017-busem,
    title = "{BUSEM} at {S}em{E}val-2017 Task 4{A} Sentiment Analysis with Word Embedding and Long Short Term Memory {RNN} Approaches",
    author = "Ayata, Deger  and
      Saraclar, Murat  and
      Ozgur, Arzucan",
    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/Ingest-2025-COMPUTEL/S17-2131/",
    doi = "10.18653/v1/S17-2131",
    pages = "777--783",
    abstract = "This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algorithms for classification of Twitter messages into negative, neutral and positive polarity. The second system is based on Long Short Term Memory Recurrent Neural Networks and uses word indexes as sequence of inputs for feature representation."
}