ENHANCED SENTIMENT DETECTION WITH XLNET & NATURAL LANGUAGE PROCESSING

Abstract

With the rapid expansion of digital communica-
tion platforms, an overwhelming amount of opinion-based con-
tent—ranging from consumer feedback to social media ex-
pressions—requires efficient interpretation for decision-making
processes. This study introduces a sentiment classification frame-
work that leverages XLNet, an advanced transformer-based
model, optimized for multi-class sentiment prediction tasks.
XLNet distinguishes itself from prior architectures by utilizing a
permutation-driven training paradigm, which enhances its ability
to model intricate linguistic patterns, including sarcasm and
domain-specific language.
TThe offered system combines Natural Language Processing
(NLP) workflow that includes data cleaning, tokenization, model
fine-tuning, and real-time deployment processes which can be
reached through a web interface based on Flask framework.
Empirical analysis on wide-range datasets demonstrates a high
level of accuracy of the model, making a score of about 86.5
percent on validation data. It is particularly noteworthy that
the sarcasm handling and multilingual processing make the
system effective in various contexts and in many languages. This
framework is scalable and flexible and, as such, can be applied
to consumer sentiment tracking, social media assessment, and
corporate intelligence collection.