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Descripción
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| 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. | |
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Internacional
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No |
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Nombre congreso
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TASS 2017: Workshop on Semantic Analysis at SEPLN |
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Tipo de participación
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960 |
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Lugar del congreso
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Revisores
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Si |
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ISBN o ISSN
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1613-0073 |
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DOI
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Fecha inicio congreso
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20/09/2017 |
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Fecha fin congreso
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22/09/2017 |
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Desde la página
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71 |
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Hasta la página
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76 |
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Título de las actas
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Proceedings of TASS 2017: Workshop on Sentiment Analysis at SEPLN co-located with 33nd SEPLN Conference (SEPLN 2017) |