An Ontology-Based Mining of Consumer Feedbacks Using Fuzzy Reasoning

Text analytics on consumer-generated content has gained significant momentum over last few years. A wide-range of text mining techniques has been proposed which can provide interesting insights about the text content. But, the challenge still exists in consuming the extracted information in form of actionable intelligence. Identifying actionable intelligence is difficult due to differences in consumer and business languages. Since feedbacks rarely talks of a single problem, determining the problems is also challenging. We propose a framework to address some of these challenges. Organizational websites or standard domain-ontologies are rich repositories of domain knowledge. The proposed method utilizes this knowledge to learn a discriminative classifier model for a domain using Fisher's discriminant metric. The consumer feedbacks are classified to different business categories using the learnt model. The output is further fed into a fuzzy reasoning unit where every feedback is assigned confidence values for each category. Initial experiments show that the proposed framework is capable of handling text feedbacks containing customer complaints in various domains.