DETECTING POTENTIAL VEHICLE CONCERNS USING NATURAL LANGUAGE PROCESSING APPLIED TO AUTOMOTIVE BIG DATA

A large volume of unstructured data exists in the automotive industry and needs to be analyzed to detect potential vehicle concerns. Much of this data is textual in nature since customer complaints are made through call center interactions and warranty repairs. Current approaches to detect potential vehicle concerns in text data include various keyword search methods. In this paper, we apply Natural Language Processing (NLP) and shallow machine learning methods on text data to create classifiers to detect the potential vehicle concern of airbag non-deployment. For this potential vehicle concern, we show the performance of multinomial Naïve Bayes (NB), Support Vector Machine (SVM) and Gradient Boosted Trees (GBT) classifiers against keyword search methods. We present challenges of classification model development related to the nature of automotive data and limited training data. Our findings provide insights on robust text classification approaches that can improve identification of potential vehicle concerns. INTRODUCTION Automotive corporations and the U.S. federal government [1] are driving improvements in product safety through the collection and analysis of both structured and unstructured (text) data. Despite their efforts, a common problem facing large corporations today is how to extract meaningful insights about product safety from large volumes of unstructured, noisy data that they have accumulated in many disparate systems. These data systems present clear opportunities for analyzing actionable information regarding product complaints and potential defects, but are commonly referred to as “dark data” because they are not easily analyzed due to their unstructured nature [2]. Consider the text data of vehicle warranty claims, call center transactions, and product complaints on social media; these sources all contain valuable information that may describe potential vehicle concerns, but are not represented in a relational structure that can be easily queried. In addition, large corporations, such as General Motors (GM), spend resources maintaining these data systems and encounter challenges efficiently extracting actionable information from them because these systems were not originally created for safety event detection. At the same time, the U.S. federal government has created several incident reporting and complaint collection systems for a variety of industries: the Food and Drug Administration’s (FDA) Adverse Event Reporting System (AERS) [3] for the pharmaceutical industry, the Federal Aviation Administration’s (FAA) Aviation Safety Reporting System (ASRS) [4] for the aviation industry, and the National Highway Traffic Safety Administration’s (NHTSA) Vehicle Owner Questionnaire (VOQ) [5] and Transportation Recall Enhancement, Accountability and Documentation (TREAD) [6] for the automotive industry. The effort by the U.S. federal government in creating these systems is due to the public interest in ensuring that products created by these industries are safe for consumers. Yet, the fundamental problem still exists; all of these systems contain large volumes of dark data because they all have varying degrees of unstructured data in the form of text. Ultimately, private industry and the U.S. federal government have a vested interest in developing techniques for the transformation of unstructured data into structured data to facilitate detection and monitoring of potential vehicle concerns within the automotive industry. For both private industry and the government there is a need to produce statistics that provide an overview of how certain types of product failures are reduced in response to their actions