Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis. (arXiv:2308.05476v1 [cs.CL])

Deceptive text classification is a critical task in natural language
processing that aims to identify deceptive or fraudulent content. This study
presents a comparative analysis of machine learning and transformer-based
approaches for deceptive text classification. We investigate the effectiveness
of traditional machine learning algorithms and state-of-the-art transformer
models, such as BERT, XLNET, DistilBERT, and RoBERTa, in detecting deceptive
text. A labeled dataset consisting of deceptive and non-deceptive texts is used
for training and evaluation purposes. Through extensive experimentation, we
compare …

algorithms analysis arxiv classification identify language language processing machine machine learning machine learning algorithms natural natural language natural language processing processing study text text classification traditional machine learning transformer