Name
Abstract | ||
|---|---|---|
Joint sentiment/topic models are widely applied in detecting sentiment-aware topics on the lengthy review data and they are achieved with Latent Dirichlet Allocation (LDA) based model. Nowadays plenty of user-generated posts, e.g., tweets and E-commerce short reviews, are published on the social media and the posts imply the public's sentiments (i.e., positive and negative) towards various topics. However, the existing sentiment/topic models are not applicable to detect sentiment-aware topics on the posts, i.e., short texts, because applying the models to the short texts directly will suffer from the context sparsity problem. In this paper, we propose a Time-User Sentiment/Topic Latent Dirichlet Allocation (TUS-LDA) which aggregates posts in the same timeslice or user as a pseudo-document to alleviate the context sparsity problem. Moreover, we design approaches for parameter inference and incorporating prior knowledge into TUS-LDA. Experiments on the Sentiment140 and tweets of electronic products from Twitter7 show that TUS-LDA outperforms previous models in the tasks of sentiment classification and sentiment-aware topic extraction. Finally, we visualize the sentiment-aware topics discovered by TUS-LDA. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-338 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Latent Dirichlet allocation,Social media,Information retrieval,Inference,Computer science,Artificial intelligence,Topic model,Machine learning | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 4 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kang Xu | 1 | 7 | 0.75 |
| Guilin Qi | 2 | 961 | 88.58 |
| Junheng Huang | 3 | 9 | 2.19 |
| Tianxing Wu | 4 | 22 | 5.42 |