  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.
