[Objective] This paper proposes a Document Influence Model (DIM) based on Dynamic Automatic Time, aiming to solve the time window dividing issue of dynamic topic model. [Methods] Firstly, we processed the text corpora with the traditional LDA model and word vector model. Secondly, we constructed a comprehensive index reflecting the differences between time windows and similarity within the time windows. Finally, we built a new model based on this index and conducted an empirical study with news corpus of the “Belt and Road” International Cooperation Summit Forum. [Results] The proposed model could quickly and effectively divide the time windows, which not only ensured the comparability of the topics under different windows, but also evaluated the influence factors of the document. [Limitations] We built the similarity index of time windows based on the traditional LDA model, which could be improved by the latest LDA models. [Conclusions] The new model is able to divide the time series text effectively, which improves the performance of traditional dynamic topic model.
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Wang Tingting,Wang Yu,Qin Linjie. Dividing Time Windows of Dynamic Topic Model. Data Analysis and Knowledge Discovery, 2018, 2(10): 54-64.
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