The Mapping Cultural Sentiments in Indonesian Digital Literature: An Annotated and Validated Multicultural Dataset - Suarni Syam Saguni; Nensilianti; Ridwan (Digital Technologies Research and Applications, 2026)
This study develops an annotated and validated multicultural sentiment dataset derived from Indonesian digital literature. The study integrates Cultural Sentiment Analysis (CSA) and Critical Discourse Analysis (CDA) to address a significant gap in existing research. No prior corpus has systematically combined cultural affective dimensions with linguistic and ethnographic validation, making this dataset crucial for mapping cultural value representations in a multicultural context. The corpus includes over 100 digital literary texts—short stories, online novels, and poems—sourced from platforms such as Wattpad, KBM App, and scholarly blogs, selected through purposive sampling to ensure diverse ethnic and thematic coverage. Annotation was carried out by trained annotators using a culturally grounded emotion lexicon, identifying sentiment polarity (positive, negative, and neutral), cultural values (social harmony, cooperation, spirituality, resistance, and adaptation), and linguistic indicators. Validation involved linguistic review for semantic accuracy and ethnographic verification through Focus Group Discussions with cultural experts from various ethnic groups. The resulting multi-layered dataset provides authentic, contextually grounded, and bias-mitigated representations of cultural sentiment in Indonesian digital literature. Beyond enriching digital humanities scholarship, it offers a reusable open resource for future research, automated sentiment analysis development, and data-driven policy formulation, all aimed at enhancing digital cultural literacy and intercultural understanding in Indonesia.