A Topic BiLSTM Model for Sentiment Classification
文献类型：会议论文
| 作者 | Yanming Huang; Yuncheng Jiang; Touhidul Hasan; Qingshan Jiang; Chao Li |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 上海 |
| 英文摘要 | The Long Short Term Memory (LSTM) network is very effective for capturing sequence information which can help to analyze sentiments. However, it fails to capture the meaning of polysemous word under different contexts. In this paper, we propose topic information-based bidirectional LSTM (BiLSTM) model for sentiment classification. BiLSTM model learns topic information to obtain the sensitive representation of the polysemous word under given circumstance. The topic information is generated through a topic modeling via Latent Dirichlet Allocation (LDA). The topic information-based BiLSTM network allows the model to capture the meaning of the polysemous word and long sequence information automatically. The experimental results on real-world datasets demonstrate that the proposed method outperforms the task of benchmark sentiment classification on SemEval 2013 and IMDB. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14083]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Yanming Huang,Yuncheng Jiang,Touhidul Hasan,et al. A Topic BiLSTM Model for Sentiment Classification[C]. 见:. 上海. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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