APPLYING TEXT MINING FOR EVIDENCE-BASED POLICY: SENTIMENT AND TOPIC ANALYSIS OF INDONESIA’S RESEARCH FUNDING REFORM - Rolando Gultom; Raden Arthur Ario Lelono; Juhartono; Lianna Kusumawati; Dody Styawan; Rino; Setyo Purnomo; Agus Prihartono; Maiforlion; Maman Firmansyah; Akhmad Faishal; Danang Rita Handoko (Jurnal Statistika dan Aplikasinya, 2026)
A substantial portion of governance-relevant information in Indonesia is contained in unstructured textual artefacts such as meeting minutes and focus group discussion (FGD) notes, yet these materials remain underutilized in policy evaluation. Existing studies have not systematically examined how computational text analysis can extract policy insights from such documents. The purpose of this research is to evaluate the implementation dynamics of the RIIM funding scheme during its transition to SBK, and to generate evidence-based recommendations that support the development of a more adaptive and context-sensitive research funding framework. This study employs a text-mining approach combining exploratory word cloud visualization, lexicon-based sentiment analysis using the Bing lexicon, emotion analysis using the NRC Emotion Lexicon, and Latent Dirichlet Allocation (LDA) topic modeling. The analysis is conducted on a corpus derived from Focus Group Discussion (FGD) verbatim transcripts involving RIIM stakeholders, consisting of 13 pages and 4,188 words. The findings reveal four major issue clusters: administrative and contractual inconsistencies, ambiguity in output classification across research fields, uncertainty in financial and temporal transitions, and challenges in aligning performance evaluation with diverse research outputs. These findings inform concrete recommendations for enhancing procedural clarity, establishing explicit transitional provisions, strengthening output classification, and institutionalizing stakeholder feedback mechanisms. This study acknowledges limitations related to the use of English-based sentiment lexicons for Indonesian data and the reliance on summarized minutes rather than verbatim transcripts. The study’s originality lies in demonstrating how computational text analysis can systematically extract policy insights from unstructured governance documents, offering a novel evidence-based approach for refining Indonesia’s research funding instruments.