Abstract
Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.- Anthology ID:
- C16-1199
- 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:
- 2112–2121
- Language:
- URL:
- https://aclanthology.org/C16-1199
- DOI:
- Cite (ACL):
- Dong Zhang, Shoushan Li, Hongling Wang, and Guodong Zhou. 2016. User Classification with Multiple Textual Perspectives. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2112–2121, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- User Classification with Multiple Textual Perspectives (Zhang et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/C16-1199.pdf