%A Yue Lixin,Liu Ziqiang,Hu Zhengyin %T Evolution Analysis of Hot Topics with Trend-Prediction %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.1155 %P 22-34 %V 4 %N 6 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4857.shtml} %8 2020-06-25 %X

[Objective] The paper constructs mathematical and content prediction models based on the external and internal characteristics academic articles, aiming to analyze the evolution of trending research topics. [Methods] With the help of LDA model, we identified the needed topics and constructed their time series. Then, we determined the popular topics by mean values and linear regression fitting. Finally, we predicted the trending topics with ARIMA and Word2Vec models based on the topic intensity and content. [Results] We conducted an empirical study to evaluate our models with stem cell research in the United States. We identified popular topics and predicted their development trends. [Limitations] There might be ambiguity in interpreting the documents, because the Word2Vec model analyzes trends of theme contents based on single words. [Conclusions] The proposed method can provide better prediction results than methods based on manual interpretation.