Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA
文献类型：会议论文
| 作者 | Yang, Min; Zhu, Dingju; Mustafa, Rashed; Chow, Kam-Pui |
| 出版日期 | 2014 |
| 会议名称 | 21st European Conference on Artificial Intelligence, ECAI 2014 |
| 会议地点 | Czech Tech Univ, Prague, CZECH REPUBLIC |
| 英文摘要 | Analyzing and understanding people's sentiments towards different topics has become an interesting task due to the explosion of opinion-rich resources. In most sentiment analysis applications, sentiment lexicons play a crucial role, to be used as metadata of sentiment polarity. However, most previous works focus on discovering general-purpose sentiment lexicons. They cannot capture domain-specific sentiment words, or implicit and connotative sentiment words that are seemingly objective. In this paper, we propose a supervised sentiment-aware LDA model (ssLDA). The model uses a minimal set of domain-independent seed words and document labels to discover a domain-specific lexicon, learning a lexicon much richer and adaptive to the sentiment of specificdocument. Experiments on two publicly-available datasets (movie reviews and Obama-McCain debate dataset) show that our model is effective in constructing a comprehensive and high-quality domain-specific sentiment lexicon. Furthermore, the resulting lexicon significantly improves the performance of sentimentclassification tasks. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6068]  |
| 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 | Yang, Min,Zhu, Dingju,Mustafa, Rashed,et al. Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA[C]. 见:21st European Conference on Artificial Intelligence, ECAI 2014. Czech Tech Univ, Prague, CZECH REPUBLIC. |
入库方式： OAI收割
来源：深圳先进技术研究院
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