Discovery of research interests of authors over time using a topic model
With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web documents usually have time information such as publish years, so knowledge patterns over time can be captured by incorporating the time information. In this paper, we propose a new topic model called the Author Topic Flow (ATF) model whose objective is to capture temporal patterns of research interests of authors over time, where each topic is associated with a research domain. The design of the ATF model is based on the hypothesis that direct topic flows are better than indirect topic flows in the state of the art Temporal Author Topic (TAT) model, which is the most similar approach to ours. We prove the hypothesis by showing the effectiveness of the ATF model compared to the TAT model.
2016 International Conference on Big Data and Smart Computing (BigComp)