Description

Human emotions are a major motivational factor of human behaviors. The relationships between consumer emotions and their buying behaviors also seem well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. Propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. Fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern based web access activities.