Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis
This work addresses the problem of identifying deceptive content for NLP researchers and practitioners, but it is incremental as it primarily compares existing methods without introducing new techniques.
The study compared traditional machine learning and transformer-based models like BERT for deceptive text classification, finding that transformer models generally outperformed traditional methods in metrics such as accuracy and F1 score.
Deceptive text classification is a critical task in natural language processing that aims to identify deceptive o 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 the performance metrics, including accuracy, precision, recall, and F1 score, of the different approaches. The results of this study shed light on the strengths and limitations of machine learning and transformer-based methods for deceptive text classification, enabling researchers and practitioners to make informed decisions when dealing with deceptive content.