@conference{recurrent-tass2017-gsi,
author = "Araque, Oscar and Barbado, Rodrigo and S{\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
abstract = "This article presents the participation of the Intelligent Systems Group (GSI) at Universidad Polit ́ecnica de Madrid (UPM) in the Sentiment Analysis work- shop focused in Spanish tweets, TASS2017.  We have worked on Task 1, aiming to classify sentiment polarity of Spanish tweets.  For this task we propose a Recurrent Neural Network (RNN) architecture composed of Long Short-Term Memory (LSTM) cells followed by a feedforward network.  The architecture makes use of two different types of features:  word embeddings and sentiment lexicon values.  The recurrent ar- chitecture allows us to process text sequences of different lengths, while the lexicon inserts directly into the system sentiment information.  The results indicate that this feature combination leads to enhanced sentiment analysis performances.",
editor = "Ceur WS",
issn = "1613-0073",
keywords = "sentiment analysis;deep learning;natural language processing;recurrent neural networks",
title = "{A}pplying {R}ecurrent {N}eural {N}etworks to {S}entiment {A}nalysis of {S}panish {T}weets",
url = "http://ceur-ws.org/Vol-1896/p8_gsi_tass2017.pdf",
volume = "1896 ",
year = "2017",
}