  Topic detection is a challenging task, especially without knowing the exact
number of topics. In this paper, we present a novel approach based on neural
network to detect topics in the micro-blogging dataset. We use an unsupervised
neural sentence embedding model to map the blogs to an embedding space. Our
model is a weighted power mean word embedding model, and the weights are
calculated by attention mechanism. Experimental result shows our embedding
method performs better than baselines in sentence clustering. In addition, we
propose an improved clustering algorithm referred as relationship-aware DBSCAN
(RADBSCAN). It can discover topics from a micro-blogging dataset, and the topic
number depends on dataset character itself. Moreover, in order to solve the
problem of parameters sensitive, we take blog forwarding relationship as a
bridge of two independent clusters. Finally, we validate our approach on a
dataset from sina micro-blog. The result shows that we can detect all the
topics successfully and extract keywords in each topic.
