Cross-Domain Sentiment Classification using Vector Embedded Domain Representations
Nicolaj Filrup Rasmussen, Kristian Nørgaard Jensen, Marco Placenti, Thai Wang
Abstract
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 domain parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.- Anthology ID:
- W19-6206
- Volume:
- Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing
- Month:
- September
- Year:
- 2019
- Address:
- Turku, Finland
- Editors:
- Joakim Nivre, Leon Derczynski, Filip Ginter, Bjørn Lindi, Stephan Oepen, Anders Søgaard, Jörg Tidemann
- Venue:
- NoDaLiDa
- SIG:
- Publisher:
- Linköping University Electronic Press
- Note:
- Pages:
- 48–57
- Language:
- URL:
- https://aclanthology.org/W19-6206
- DOI:
- Cite (ACL):
- Nicolaj Filrup Rasmussen, Kristian Nørgaard Jensen, Marco Placenti, and Thai Wang. 2019. Cross-Domain Sentiment Classification using Vector Embedded Domain Representations. In Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing, pages 48–57, Turku, Finland. Linköping University Electronic Press.
- Cite (Informal):
- Cross-Domain Sentiment Classification using Vector Embedded Domain Representations (Rasmussen et al., NoDaLiDa 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/W19-6206.pdf