ARTIFICIAL INTELLIGENCE BASED ANALYTICAL FRAMEWORK FOR MEASURING BRAND TRUST AND AUTHENTICITY

Abstract

The modern digital marketplace requires brands to
establish trust with consumers while maintaining
their authentic image because these factors
determine customer engagement and brand success
over time. Consumers produce substantial a mounts
of textual data through their online activities which
include writing product reviews and interacting
with content on social media platforms and
participating in internet conversations.
Conventional approaches for assessing brand
perception do not s ucceed in capturing the evolving
customer emotional responses which exist in
contemporary branding situations. The study
presents an Artificial Intelligence analytical
framework which uses business analytics methods
to measure brand trust and authenticity through AI
analysis of consumer generated online content. The
framework uses a systematic approach to examine
consumer textual responses which enables the
discovery of sentiment patterns and trust indicators
and authenticity signals that exist in online
di alogues. The study shows how artificial
intelligence can create measurable brand credibility
indicators by processing unstructured consumer
expressions through its four steps which involve
digital data analysis and trust detection through
sentiment assessm ent and authenticity signal
extraction and brand perception classification. The