Title: Understanding research trends based on paper abstracts using topic modeling
Authors: Mio Takei - The Institute of Statistical Mathematics (Japan) [presenting]
Tomokazu Fujino - Fukuoka Women University (Japan)
Keisuke Honda - The Institute of Statistical Mathematics (Japan)
Junji Nakano - The Institute of Statistical Mathematics (Japan)
Abstract: With the scale of real data becoming larger, the statistical science has become also important in the general society. We investigate research movements and trends in statistical science in the academic filed using Latent Dirichlet Allocation (LDA). Abstracts from academic papers, which is the most objective output of research activities, are available by Web of Science (WoS) and are good data to see trends of research. Therefore, we analyzed these data for understanding current research movements and trends in statistical science. We applied LDA model on paper abstracts and estimated topics. We then aggregated these topic distributions over time to find research movements. Our results can be used to understand the topics in statistical science that will be hot in the future (and also cold in the future). We can use these results to determining the special themes in our institute research to promote active statistical research in Japan. Furthermore, we can help researchers to discover promising research topics in the future.