Detecting bursts in sentiment-aware topics from social media

Nowadays plenty of user-generated posts, e.g., sina weibos, are published on the social media. The posts contain the public's sentiments (i.e., positive or negative) towards various topics. Bursty sentiment-aware topics from these posts reveal sentiment-aware events which have attracted much attention. To detect sentiment-aware topics, we attempt to utilize Joint Sentiment/Topic models, these models are achieved with Latent Dirichlet Allocation (LDA) based models. However, most of the existing sentiment/topic models cannot be directly utilized to detect sentiment-aware topics on the posts, since applying the models to the posts directly suffers from the context sparsity problem. In this paper, we propose a Time-User Sentiment/Topic Latent Dirichlet Allocation (TUS-LDA) which simultaneously models sentiments and topics for posts. Thereinto, TUS-LDA aggregates posts in the same timeslices or from the same users as pseudo-documents to alleviate the context sparsity problem. Based on TUS-LDA, we further design an approach to detect bursty sentiment-aware topics and these sentiment-ware topics can reflect bursty real-world events. Experiments on the Chinese sina weibos show that TUS-LDA outperforms previous models in the tasks of sentiment classification and burst detection in sentiment-aware topics. Finally, we visualize the bursty sentiment-aware topics discovered by TUS-LDA.