关键词: 学科主题预测, 热度预测, 期刊影响因子, 长短期记忆神经网络, 图书馆与信息科学

Abstract: ［Purpose/Significance］As an important part of prediction in science of science, scientific topic popularity prediction contributes to reveal hot topics and discover development trends. It is helpful for scholars to find the cutting-edge topics, and assist scientific research management institutions to fund projects reasonally.［Design/Methodology］This paper proposed a Topic Popularity Computing Model based on Journal Impact Factor (TP-JIF), and constructed a scientific topic prediction model based on LSTM. Taking LIS as an example, this study extracted the topics via LDA and author keywords, computed the popularity time series, and designed experiments to verify the model in different time lengths.［Findings/Conclusion］ It is found that when comparing with the RBF-SVM, Linear-SVM, KNN, and Naive Bayesian, the prediction model of LSTM can well present the characteristics of time series for scientific topic popularity, and the prediction result turns out to be the best when the length of time series is four years.
［Originality/Value］A novel computing model of scientific topic popularity based upon journal impact factors has been proposed, which could depict the differences of journal impacts in academic fields, and avoid the disadvantages of considering frequency as the only influential factor. The proposed popularity prediction model in this study could offer an excellent representation of time-changing features for scientific topics, reduce the prediction errors, and provide good prediction.

Keywords: Scientific topic prediction, Popularity prediction, Journal impact factor, Long short-term memory(LSTM), Library and information science(LIS)