Data Mining Algorithms for a Feature-Based Customer Review Process Model with Engineering Informatics Approach

Abstract

The data generated from online communication acts as potential gold mines for discovering knowledge for end users. Large amount of data is also generated in the form of web documents, emails, blogs, and feedback, etc. Text analytics and opinion mining are used to extract human thoughts and perceptions from unstructured texts. This paper proposes a method that focuses on analysing different classification and clustering algorithms aimed at extracting and consolidating opinions of customers from social media sites like Facebook, Twitter and through surveys, at multiple levels of granularity to monitor and measure customer satisfaction. This is an automated approach, in which algorithms aid in the process of knowledge assimilation identification and the analytics. Domain experts ratify the knowledge base and provide training data sets for the system to intuitively gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive, negative or neutral. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be congregated at any desired level of specificity i.e. feature level or product level, user level or service level, etc. It has been found that J48 classification algorithm and simple k-means clustering algorithm are most suitable for restaurant customer reviews.