Customer Reviews flow in across multiple sources in thousands per day. These reviews could be in the form of a transcribed telephone conversation, an online review or, a survey. We want to extract cases of Product Safety from this huge amount of data. Product Safety issues cover less than 1% of the total reviews which may be as high as 70K per day coming from a singular data source. Manual review of these cases to identify potential product-safety threats poses the risk of missing out on such rare events, which may later back-fire on the industry jeopardizing its reputation and liability. Preventive measures, under such circumstances, are extremely crucial and time-sensitive.

Unlike simple cases of imbalanced binary classification, the challenge here is to gather training data. Available training data consists of very few Reportable cases, pertaining to Product Safety, and no Non-Reportable case. This is an extreme case of PU Classification where the class, for which instances are available, is highly imbalanced. However, every PU algorithm would require a lot of instances of the Positive Class, in our case, Reportables. In real life, instances of such rare events are difficult to come by and hence, obtaining enough training data to run a PU Classification is a luxury. In iCASSTLE, we propose a two-staged classification where Stage I leverages three unique components of text mining to capture a representative training data containing instances of both classes in the right proportions, and Stage II leverages results from Stage I to be fed into a semi-supervised set-up. The final Classification is given by Stage II and a top percentage of Stage I results. Furthermore, Reportables are arranged in order of a metric indicating the degree of Reportability. We applied this to multiple datasets differing in nature of Reportables as well as nature of imbalance. iCASSTLE was able to ensure recall maximisation of all such diverse datasets and therein lies its applicability.

In the fast moving world today, rare events are becoming increasingly common. Ranging from studying incidents of safety hazards to identifying transaction fraud, they all fall under the radar of rare events. Identifying and studying rare events become of crucial importance, particularly when the underlying event conforms to a sensitive or an adverse issue. The thing to note here is, despite the probability of occurrence being very close to zero, the potential specification of the rare event could be quite extensive. For example, within the parent rare event of Product Safety, there could be multiple types of potential hazard (Fire, Electrical, Pharmaceutical, etc.), rendering the sub-classes rarer still. In this talk, we are going to discuss a novel algorithm designed to study a rare event and its sub-classes over time with primary focus on forecast and detecting anomalies.

The anomalies studied here are relative anomalies i.e., they may not contribute to the long-term trend of the rare time series but represent deviation from the base state as seen in the immediate past.

Keywords:

Product Safety

Fire Hazards

Transaction Fraud

Malware Detection

Relevant Sister Classes

Count Time Series

Non Homogeneous Poisson Process

Discrete Space Optimization

Text Mining

GloVe

Sparsity Treatment in Text

Dynamic Time Warping

Discords

Anomaly Detection

Deviation Score

Relative Local Density

Density Based Clustering