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Descripción
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| entiment lexicons are widely used in computational linguistics, as they represent a resource that directly contains subjective sentimental knowledge. Usually these sentiment lex- ica are generic and developed without any specific semantic domain in mind. Nonetheless, the domain context can be highly relevant for sentiment analysis, as it is known that word polarities can be influenced by domain-specific traits. This paper studies the problem of automatically generating domain- adapted sentiment lexicons that can be used in posterior senti- ment analysis tasks. We propose a neural network approach that modifies a sentiment lexicon using distantly annotated text of a certain domain. Additionally, we present a completely data-driven domain characterization metric that measures the centrality of a set of documents. Experimental work shows that this metric offers a measure of the generated lexicons? quality. Also, it is shown that the generated lexicons yield higher performance on domain-oriented sentiment analysis than a generic lexicon and other known baselines. Finally, it is also discussed that these extracted lexicons can be used for sentiment analysis even for approaches with no learning capabilities. | |
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Internacional
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Si |
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Nombre congreso
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2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) |
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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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978-1-5386-0680-3 |
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DOI
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10.1109/ACIIW.2017.8272598 |
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Fecha inicio congreso
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23/10/2017 |
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Fecha fin congreso
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26/10/2017 |
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Desde la página
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105 |
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Hasta la página
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110 |
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Título de las actas
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Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 2017 Seventh International Conference on |