Conference article

Cross-Domain Sentiment Classification using Vector Embedded Domain Representations

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

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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.


sentiment classification, vector embedding, cross-domain


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