Descripción
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This paper describes our participation at SemEval- 2014 sentiment analysis task, in both contextual and message polarity classification. Our idea was to com- pare two different techniques for sentiment analysis. First, a machine learning classifier specifically built for the task using the provided training corpus. On the other hand, a lexicon-based approach using natural language processing techniques, developed for a ge- neric sentiment analysis task with no adaptation to the provided training corpus. Results, though far from the best runs, prove that the generic model is more robust as it achieves a more balanced evaluation for message polarity along the different test sets. | |
Internacional
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Si |
Nombre congreso
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8th International Workshop on Semantic Evaluation (SemEval ''14) |
Tipo de participación
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960 |
Lugar del congreso
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Dublin, Ireland |
Revisores
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Si |
ISBN o ISSN
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978-1-941643-24-2 |
DOI
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Fecha inicio congreso
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23/08/2014 |
Fecha fin congreso
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24/08/2014 |
Desde la página
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218 |
Hasta la página
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222 |
Título de las actas
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Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval ''14) |