A Novel Approach to Analyse User Satisfaction Level On Web pages using Ontologies

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Web access log analysis is to analyze the patterns of web site usage and the features of user’s behavior. The proposed method constructs sessions as a Directed Acyclic Graph which contains pages with calculated weights. This will help site administrators to find the interesting pages for users and to redesign their web pages. After Session Construction a web usage analysis is used for finding the correlation between consumer emotions and buying behaviors. A semantic web usage mining technique is proposed for finding web access patterns from the annotated web usage logs. It includes consumer emotions and behaviors via self-reporting and behavioral tracking. To signify the real-time temporal concepts and requested resource attributes of periodic pattern based web access activities fuzzy logic is used. The consumer emotions and behaviors are integrated into a Personal Web Usage Lattice which represents the web access activities. From this we create Personal Web usage Ontology which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction level. So, in this manuscript an innovative technique is introduced which is called Optimum Session Interval based Particle Swarm Optimization(OSIPSO). This technique is used to find the optimum session interval. Additionally, an associative classification is used to enhance the level of accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work achieves high accuracy and highly efficient in terms of user satisfaction level.