Feature-based Facebook reviews process model for e-management using data mining

Abstract

The data generated from online communication acts as potential gold mines for discovering knowledge for researchers. A large amount of data is also generated in the form of web documents, emails, blogs, and feedback, etc. Text analytics is being significantly employed to mine important information. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts. The showstopper for designing an opinion mining system for analyzing reviews arise from the fact that customer reviews are often noisy. These reviews are informally written. In addition, they are subjected to spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on analyzing the 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. Ours is an automated approach, in which the system aids in the process of knowledge assimilation for knowledge-based building and also performs the analytics. Domain experts ratify the knowledge base and also provide training datasets 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. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined datasets.