An Artificial Intelligence–Based Framework for Automated Framing Analysis of Online Media Texts

These articles were then converted into raw text (.txt) files using the ocr_pdftotxt.r script with RStudio, which leverages the tesseract package for Optical Character Recognition (OCR). Subsequently, the buat_corpus_baru_txt.r script was used to create a quanteda corpus object from the .txt files with RStudio, while also performing initial pre-processing (tokenisation, stopword removal, etc.).

A word frequency table was then generated using buat_tabel_rds.r and further refined by normalising synonyms (ganti_sinonim_dgn_list.r) and removing irrelevant words (hapus_kata_dgn_list.r). From this clean table, 98 keywords were selected and manually classified into Entman's four framing dimensions (problem definition, causal interpretation, moral evaluation, and treatment recommendation), with the results saved in separate .csv files for each dimension.