Abstract
Sentiment classification becomes more and more important with the rapid growth of user generated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent and training sentiment classifier for every domain is inefficient and often impractical; second, since the quantity of labeled data is important for assessing the quality of classifier, it is hard to evaluate classifiers when labeled data is limited for certain domains. To address the challenges mentioned above, we focus on learning high-level features that are able to generalize across domains, so a global classifier can benefit with a simple combination of documents from multiple domains. In this paper, the proposed model incorporates both sentiment polarity and unlabeled data from multiple domains and learns new feature representations. Our model doesn’t require labels from every domain, which means the learned feature representation can be generalized for sentiment domain adaptation. In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features. Empirical evaluations demonstrate our model outperforms baselines and yields competitive results to other state-of-the-art works on benchmark datasets.- Anthology ID:
- C16-1280
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2978–2988
- Language:
- URL:
- https://aclanthology.org/C16-1280
- DOI:
- Cite (ACL):
- Fan Yang, Arjun Mukherjee, and Yifan Zhang. 2016. Leveraging Multiple Domains for Sentiment Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2978–2988, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Leveraging Multiple Domains for Sentiment Classification (Yang et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/C16-1280.pdf
- Data
- Multi-Domain Sentiment