Abstract
The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited. However, current models do not deal with the cold-start problem which is typical in review websites. In this paper, we present Hybrid Contextualized Sentiment Classifier (HCSC), which contains two modules: (1) a fast word encoder that returns word vectors embedded with short and long range dependency features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism that considers the existence of cold-start problem when attentively pooling the encoded word vectors. HCSC introduces shared vectors that are constructed from similar users/products, and are used when the original distinct vectors do not have sufficient information (i.e. cold-start). This is decided by a frequency-guided selective gate vector. Our experiments show that in terms of RMSE, HCSC performs significantly better when compared with on famous datasets, despite having less complexity, and thus can be trained much faster. More importantly, our model performs significantly better than previous models when the training data is sparse and has cold-start problems.- Anthology ID:
- P18-1236
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2535–2544
- Language:
- URL:
- https://aclanthology.org/P18-1236
- DOI:
- 10.18653/v1/P18-1236
- Cite (ACL):
- Reinald Kim Amplayo, Jihyeok Kim, Sua Sung, and Seung-won Hwang. 2018. Cold-Start Aware User and Product Attention for Sentiment Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2535–2544, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Cold-Start Aware User and Product Attention for Sentiment Classification (Amplayo et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-1236.pdf
- Code
- rktamplayo/HCSC
- Data
- IMDb Movie Reviews