Cross-Domain Sentiment Classification Using Web Usage Mining

Web usage mining is crucial for the Cross-Domain Sentiment Classification (CDSC) as it can ensure customer satisfaction as far as the interaction between the customer and the organization is concerned. Web usage mining is also helpful for identifying or improving the visitors of a particular Website by accessing the log file of that site. In this paper the focus is on Web usage mining of Log data of an educational institution. Web usage mining (WUM) also known as Web Log Mining is the application of Data Mining. WUM techniques are applied on large volume of data to extract useful and interesting patterns from Web data, specifically from web logs, in order to improve web based applications. Web usage mining consists of four phases, data source, pre-processing, pattern discovery, and pattern analysis. After the completion of these four phases the user can find the required usage patterns and use this information for the specific needs in a variety of ways such as improvement of the Web application, identifying the visitor's behaviour, customer attraction, Customer retention etc..