iCASSTLE : Imbalanced Classification Algorithm for Semi Supervised Text Learning

Information in the form of text can be found in abundance in the web today, which can be mined to solve multifarious problems. Customer reviews, for instance, flow in across multiple sources in thousands per day which can be leveraged to obtain several insights. Our goal is to extract cases of a rare event e.g., recall of products, allegations of ethics or, legal concerns or, threats to product-safety, etc. from this enormous amount of data. Manual identification of such cases to be reported is extremely labour-intensive as well as time-sensitive, but failure to do so can have fatal impact on the industry's overall health and dependability; missing out on even a single case may lead to huge penalties in terms of customer experience, product liability and industry reputation. In this paper, we will discuss classification through Positive and Unlabeled data, PU classification, where the only class, for which instances are available, is a rare event. In iCASSTLE, we propose a two-staged approach where Stage I leverages three unique components of text mining to procure representative training data containing instances of both classes in the right proportion, and Stage II uses results from Stage I to run a semi-supervised classification. We applied this to multiple datasets differing in nature of Product Safety as well as nature of imbalance and iCASSTLE is proven to perform better than the state-of-the-art methods for the relevant use-cases.