Long Nights, Rainy Days, and Misspent Youth: Automatically Extracting and Categorizing Occasions Associated with Consumer Products

摘要

One way in which marketers gain insights about consumers is by identifying the occasions in which consumers use their products and which are invoked by their products. Identifying occasions helps in consumer segmentation, answering why consumers purchase a product, and where and when they use it. Additionally, the types of occasions a consumer participates in and the social settings surrounding those occasions provide insights into the consumer's personality and sociocul-tural self. Insights such as these are required for understanding consumer behavior, which marketers need to better design and sell their products. In this paper, we describe a methodology for extracting and categorizing occasions from product reviews, product descriptions, and forum posts. We examine using a maximum entropy markov model (MEMM) and a linear chain conditional random field (CRF) for extraction and find the CRF results in a 72.4% F1-measure. Extracted occasions are categorized as one of six high-level types (Celebratory, Special, Seasonal, Temporal, Weather-Related, and Other) using a support vector machine with an 88.5% macro-averaged F1-measure.