Abstract
In order to help investors get effective information from a large amount of financial news data as soon as possible, a topic detection model based on multi-view text semantics and clustering topic detection algorithm is proposed. In the financial news data set, different models are used to extract the characteristics of the news, and the characteristics of various models are merged. The clustering algorithm is improved by introducing JS divergence and time decay factors. The experimental results show that compared with the traditional topic detection model, the proposed method has higher accuracy of topic detection and shorter runtime of clustering algorithm.
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