Vietnamese Facebook Posts Classification using Fine-Tuning BERT

RESEARCH CREW
0:12 11/03/2021

With the development of social networks in the age of information technology explosion, the classification of social news plays an important role in detecting the hot topics being discussed on social networks over a period of time. In this paper, we present a new model for effective Facebook's posts classification and a new dataset which is labeled for the corresponding subject. The dataset consists of 5191 Facebook user's public posts, which is divided into 3 subsets: training, validation and testing data sets. Then, we explore the effectiveness of fine-tuning BERT model with three truncation methods compared with other machine learning algorithms on our dataset. Experimental results show that the fine-tune BERT models outperform other approaches. The fine-tune BERT with “head + tail” truncation methods achieves the best scores with 84.31% of Precision, 84.12% of Recall and 84.15% of F1-score.

TIN LIÊN QUAN
The abuse of prescription medications has become a severe public health crisis fueled by limited coordination and oversight across healthcare systems. Current frameworks lack interoperability between doctors, pharmacies, and regulators, enabling abusive practices like doctor shopping and pharmacy hopping. To address these issues, this research proposes Medichain, a novel multichain-based...