Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis

TL;DR

This paper presents a framework utilizing text mining and sentiment analysis on social media data to monitor product safety, aiding stakeholders in early detection of adverse events and counterfeit issues.

Contribution

It introduces a novel framework for analyzing social media content for product safety, including developing a sentiment classifier and a product safety lexicon.

Findings

Text mining and sentiment analysis effectively identify safety concerns.

Machine learning classifier predicts sentiment orientation with promising accuracy.

Social media analysis helps monitor brand and product sentiment trends.

Abstract

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.