Topic modeling for management sciences: A network-based approach

Big data mining and unsupervised pattern recognition from large corpus of text-based documents has been an active research topic over the past decade. This paper presents a novel sequence of network-based models for identifying high-dimensional clustering patterns between topics for quantitative and predictive modeling of trends in Management Science (an INFORMS Journal) papers over the past 54 years. The proposed methods extrapolate a new spatial dimension from publication records to identify and assess topic inter-dependence and clustering trends over time. First, the optimal number of topics for trend analysis is identified based on spatial clustering patterns using Self-Organizing-Maps (SOM). Next, topic models are used to construct weighted and unweighted complex networks. Based on spatio-temporal clustering trends in the complex networks, the influence, importance and uniqueness of topics are quantified. Finally, the dynamic trends in topic influence are modeled for predictive purposes using Hidden Markov Models (HMM). The proposed methods provide insights into topic type co-existence patterns, topic type rankings, identify ~40% topics as unique and predict topic importance with average accuracy per topic in the range of 79-84%. Thus, the proposed methods provide the apparatus to translate time-series text-intensive data sets to spatio-temporal models that can provide additional insights on data interdependencies and inter-data influences.