Analyzing Evolution of News Topics with Manifold Learning
Xu Yuemei1(),Li Yang2,3,Liang Ye1,Cai Lianqiao1
1Department of Computer Science, Beijing Foreign Studies University, Beijing 100089, China 2Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China 3University of Chinese Academy of Sciences, Beijing 100049, China
[Objective] This study aims to examine the creation and development of online news topics, and then to gauge the public opinion. [Methods] First, we introduced the manifold learning technology to analyze the news topics. Second, we explored the relations among the high dimensional topics from each time window, which were identified by the LDA model. Third, we clustered these topics and visualized the relations among them in the low-dimensional space. Finally, we analyzed the topic evolution with the help of social network theorem. [Results] The proposed method could effectively identify the topic evolution trends of news reports on China from CNN in 2015. [Limitations] We did not fully explore the impacts of time windows. [Conclusions] This study provides a new method to visualize the evolution of news report topics over a period of time, which avoids inaccurate description due to the changing of adjacent time windows.
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Xu Yuemei,Li Yang,Liang Ye,Cai Lianqiao. Analyzing Evolution of News Topics with Manifold Learning. New Technology of Library and Information Service, 2016, 32(10): 59-69.
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