Nicolaj Filrup Rasmussen
IT University of Copenhagen, Denmark
Kristian Nørgaard Jensen
IT University of Copenhagen, Denmark
Marco Placenti
IT University of Copenhagen, Denmark
Thai Wang
IT University of Copenhagen, Denmark
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Published in: DL4NLP 2019. Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing, 30 September, 2019, University of Turku, Turku, Finland
Linköping Electronic Conference Proceedings 163:6, p. 48-57
NEALT Proceedings Series 38:6, p. 48-57
Published: 2019-09-27
ISBN: 978-91-7929-999-6
ISSN: 1650-3686 (print), 1650-3740 (online)
Due to the differences between reviews in different product categories, creating a general model for cross-domain sentiment classification can be a difficult task. This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model. In addition to providing a cross-domain model, this also provides a quantifiable representation of the domains as numeric vectors. We show that it is possible to cluster the domain vectors and provide qualitative insights into the inter-domain relations. We also a) present a new data set for sentiment classification that includes a do main parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.
