Potential and Limitations of Cross-Domain Sentiment Classification
Jan Milan Deriu, Martin Weilenmann, Dirk Von Gruenigen, Mark Cieliebak
Abstract
In this paper we investigate the cross-domain performance of a current state-of-the-art sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains.- Anthology ID:
- W17-1103
- Volume:
- Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Lun-Wei Ku, Cheng-Te Li
- Venue:
- SocialNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–24
- Language:
- URL:
- https://aclanthology.org/W17-1103
- DOI:
- 10.18653/v1/W17-1103
- Cite (ACL):
- Jan Milan Deriu, Martin Weilenmann, Dirk Von Gruenigen, and Mark Cieliebak. 2017. Potential and Limitations of Cross-Domain Sentiment Classification. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 17–24, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Potential and Limitations of Cross-Domain Sentiment Classification (Deriu et al., SocialNLP 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-1103.pdf