title: Automated discovery of safety and efficacy concerns for joint & muscle pain relief treatments from online reviews.
abstract: Product issues can cost companies millions in lawsuits and have devastating effects on a firm's sales, image and goodwill, especially in the era of social media. The ability for a system to detect the presence of safety and efficacy (S&E) concerns early on could not only protect consumers from injuries due to safety hazards, but could also mitigate financial damage to the manufacturer. Prior studies in the field of automated defect discovery have found industry-specific techniques appropriate to the automotive, consumer electronics, home appliance, and toy industries, but have not investigated pain relief medicines and medical devices. In this study, we focus specifically on automated discovery of S&E concerns in over-the-counter (OTC) joint and muscle pain relief remedies and devices.
  
  
  
  We select a dataset of over 32,000 records for three categories of Joint & Muscle Pain Relief treatments from Amazon's online product reviews, and train "smoke word" dictionaries which we use to score holdout reviews, for the presence of safety and efficacy issues. We also score using conventional sentiment analysis techniques.
  
  
  
  Compared to traditional sentiment analysis techniques, we found that smoke term dictionaries were better suited to detect product concerns from online consumer reviews, and significantly outperformed the sentiment analysis techniques in uncovering both efficacy and safety concerns, across all product subcategories.
  
  
  
  Our research can be applied to the healthcare and pharmaceutical industry in order to detect safety and efficacy concerns, reducing risks that consumers face using these products. These findings can be highly beneficial to improving quality assurance and management in joint and muscle pain relief.
authors.affiliation: Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States. Electronic address: exzachly@vt.edu.
authors.forename: David Z
authors.initials: DZ
authors.lastname: Adams
authors.affiliation: Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States. Electronic address: rgruss@vt.edu.
authors.forename: Richard
authors.initials: R
authors.lastname: Gruss
authors.affiliation: Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States. Electronic address: abra@vt.edu.
authors.forename: Alan S
authors.initials: AS
authors.lastname: Abrahams
mesh_terms: D001331:Automation; D059350:Chronic Pain / Q000188:drug therapy*; D040261:Harm Reduction; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D063806:Myalgia / Q000188:drug therapy*; D004366:Nonprescription Drugs / Q000627:therapeutic use*; D059408:Pain Management; D012449:Safety; D012651:Self Medication / Q000523:psychology*
chemical_list: D004366:Nonprescription Drugs
keywords: Defect discovery; Quality management; Text mining