A topic model for building fine-grained domain-specific emotion lexicon
文献类型：会议论文
| 作者 | Min Yang; Baolin Peng; Zheng Chen; Dingju Zhu; Kam-Pui Chow |
| 出版日期 | 2014 |
| 会议名称 | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 |
| 会议地点 | Baltimore, MD, United states |
| 英文摘要 | Emotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecificlexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6066]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Min Yang,Baolin Peng,Zheng Chen,et al. A topic model for building fine-grained domain-specific emotion lexicon[C]. 见:52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014. Baltimore, MD, United states. |
入库方式： OAI收割
来源：深圳先进技术研究院
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