Detection of Topics and Time Series Variation in Consumer Web Communication Data

Consumers’ personal interests are influenced by new product strategies, such as marketing communication schemes, and these can change over time. Thus, it is important to consider temporal variation in trending consumer interests. We aimed to detect temporal variations in consumer web communication data using weight coefficients between entries and topics obtained from nonnegative matrix factorization. The weight coefficient, which indicates the strength between an entry and a topic, was modeled with a Bayesian network to capture changes in the topic over time. Bayesian networks, commonly used in a wide range of studies such as anomaly detection, reasoning, and time series prediction, build models from data using Bayesian inference for probability computations. The causations can be modeled by representing conditional dependence based on the edges in a directed graph of the Bayesian network.