This work examines the effectiveness of stacking traditional machine learning techniques like Logistic Regression, k-Nearest Neighbors, Random Forest, Multi-Layer Perceptron, Support Vector Classifier and simple word counting and compares them to BERT, a powerful pre-trained language model.
A hybrid approach is proposed that leverages frozen embeddings from the pre-trained language model cardiffnlp/twitter-roberta-base-offensive as a high-level semantic feature extractor, combined with the CatBoost gradient boosting algorithm as the final classifier, avoiding the computational cost associated with fine-tu...
Muhammad Elfarizi, Surya Agustian, Fitra Kurnia et al.· 0 citations
Detecting harmful language in online platforms is essential for creating safer and more inclusive social media environments. However, research on harmful language detection has largely focused on high-resource languages, while low-resource languages such as Pashto remain comparatively underexplored. This study compares...
Shahid Nasiri, Nesar Ahmad Wasi· Journal of nature and scienc...· 0 citations
This work studies a variety of retrieval-based in-context learning (RetICL) strategies for detecting defamatory offences under {\S}{\S} 185-187 StGB (the subject of GermEval 2026 Subtask 4).
Kristin Gnadt, Maximilian Meidinger, M. Aßenmacher· 0 citations
In this study, a longitudinal dataset of more than 23 million news headlines from 47 U.S.-based media outlets is used to investigate the use of large language models (LLMs) for sentiment detection. Recent developments in LLMs offer potential gains in contextual understanding, adaptability, and generalization, even thou...
Advances in digital technology have made social media the primary platform for the public to voice their opinions on government policies, including the Free Nutritious Meals (MBG) program. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest algorithms in conducting sentiment...
A. Saputra, N. Zulkarnaim, F. Wajidi· JURNAL FASILKOM· 0 citations
