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Topic models are a prevailing tool for exploring latent topics in documents, and for helping to complete many NLP tasks. To obtain good topics for a corpus, a preprocessing step is often needed to remove common stop words and identify topic general words (TGW) from the corpus. Such words can seriously harm the topic formation because they create spurious co-occurrence of unrelated words. Also, they are likely to occupy top positions of multiple topics, lead to many unrelated words being grouped under a topic, and consequently result in inscrutable and similar topics. In an application, one typically manually identifies and removes a list of TGWs in the corpus. This is a time consuming process and very hard to do by a layman user. In this paper, we aim to solve this problem automatically. The proposed approaches can be based on the current corpus alone or multiple corpora. In the latter case, a novel continuous learning method is proposed that learns from past results of multiple domain corpora to help identify TGWs in the current domain. We conduct experiments in two real-world datasets, and the experimental results show that the proposed approaches achieve superior results. [ABSTRACT FROM AUTHOR]

Traditional topic modeling has been widely studied and popularly employed in expert systems and information systems. However, traditional topic models cannot discover structural relations among topics, thus losing the chance to explore the data more deeply. Hierarchical topic modeling has the capability of learning topics, as well as discovering the hierarchical topic structure from text data. But purely unsupervised models tend to generate weak topic hierarchies. To solve this problem, we propose a novel knowledge-based hierarchical topic model (KHTM), which can incorporate prior knowledge into topic hierarchy building. A key novelty of this model is that it can mine prior knowledge automatically from the topic hierarchies of multiple domains corpora. In this paper, the knowledge is represented as the word pairs which satisfy the requirement of frequent co-occurrence, and knowledge is organized in form of hierarchical structure. We also propose an iterative learning algorithm. For evaluation, we crawled two new multi-domain datasets and conducted comprehensive experiments. The experimental results show that our algorithm and model can generate more coherent topics, and more reasonable hierarchical structure. [ABSTRACT FROM AUTHOR]

YouTube offers a great opportunity for people to entertain, advertise, gain popularity, and generate revenue. How to increase views for a video has become the key question for anyone who wish to be famous or gain more revenue. Recognizing that a recommendation system is a major view source for videos, our goal in this paper is to increase video views in YouTube by leveraging on the recommendation system. We first perform measurements to understand how views are propagated among videos through recommendation links and identify factors that influence the recommendation produced by the system. Our measurement results show that similarity in video meta-data is a crucial ingredient in connecting videos. We then propose a keyword suggestion method for a video with the aim to raise video views through the recommendation system. The keyword suggestion method utilizes video clusters on a referrer video graph to obtain relevant keywords and ranks keywords based on both their relevance and their potential to attract video views. The effectiveness of the keyword suggestion method is demonstrated through a case study, showing that using the keywords suggested by our method leads to a larger number of video views and higher average watching time per video playback compared to initial user-given keywords. [ABSTRACT FROM AUTHOR]