A Computational Approach for Identifying Keywords Related to the 2030 Agenda for Sustainable Development Goals in a Brazilian Higher Education Institution
Abstract
1. Introduction
1.1. Research Background
1.2. Research Motivation
1.3. Novelty and Differentiation from Existing Approaches
- Context-Specific Lexical Resource: Unlike generic keyword tools (e.g., [35,41]), our introduced model employs a purpose-built dictionary of 3560 Brazilian Portuguese terms derived from the 2030 Agenda’s local semantic context. Such lexicon accounts for linguistic nuances (e.g., regional poverty terminology like “pobreza extrema” vs. “pobreza moderada”) absent in English-centric models.
- Interdisciplinary Gap Bridging: Existing tools (e.g. Matsui et al. [40]) prioritize semantic mapping but lack granularity for cross-domain SDG alignment in HEIs. Our workflow quantifies discipline-specific SDG engagement (e.g., Engineering projects tied to SDG 9 vs. Health Sciences to SDG 3), enabling targeted institutional strategies.
1.4. Research Objectives
1.5. Research Contribution
2. Material and Methods
2.1. Data Preprocessing
- Accent and punctuation removal: All diacritics and punctuation were removed using Unicode normalization (unicodedata).
- Case normalization: Text was converted to uppercase for consistency.
- Stopword removal: NLTK’s Portuguese stopword list was used to eliminate common, semantically empty words.
- Whitespace and symbol filtering: Regular expressions filtered out all non-alphanumeric characters, retaining only letters, digits, and underscores.
2.2. Tokenization and Stemming
2.3. Keyword Matching and SDG Attribution
2.4. Evaluation and Validation
- Manual validation of 100 randomly selected summaries, comparing automated SDG matches with expert annotations.
- Calculation of word frequency distributions using NLTK’s FreqDist to confirm alignment with expected SDG content [56].
- Comparative analysis across years (2019 vs. 2020) and campuses to ensure consistency and robustness of results.
3. Results and Discussion
3.1. Computational Setup
3.2. Computational Approach Results
3.3. Discussion
- The keyword framework introduced by Mori Junior et al. [35] relies on English-language UN documents, thus limiting applicability in Lusophone contexts. Our Brazilian Portuguese lexicon and stemming process (e.g., by reducing “desigualdade” → “igual”) capture local vernacular absent in translation-dependent tools.
- The classification model presented by Angin et al. [41] targets corporate sustainability reports, lacking HEI-specific metrics (e.g., extension projects, teaching plans). By processing academic abstracts and institutional documents, we enable granular impact scoring (high/medium/low) tied to SDG targets.
4. Conclusions
- Revealing unexpected interdisciplinary engagements (e.g., SDG 2 in Engineering/Computer Science), challenging rigid expertise silos and fostering cross departmental partnerships;
- Providing evidence of institutional resilience during crises, as shown by sustained SDG 3 focus despite pandemic disruptions (refer to Figure 5).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Estorani Polessa, A.C.; Tavares, G.G.; Medina, R.; Saporetti, C.M.; Gontijo, T.S.; Bodini, M.; Goliatt, L.; Capriles, P. A Computational Approach for Identifying Keywords Related to the 2030 Agenda for Sustainable Development Goals in a Brazilian Higher Education Institution. Societies 2025, 15, 165. https://doi.org/10.3390/soc15060165
Estorani Polessa AC, Tavares GG, Medina R, Saporetti CM, Gontijo TS, Bodini M, Goliatt L, Capriles P. A Computational Approach for Identifying Keywords Related to the 2030 Agenda for Sustainable Development Goals in a Brazilian Higher Education Institution. Societies. 2025; 15(6):165. https://doi.org/10.3390/soc15060165
Chicago/Turabian StyleEstorani Polessa, Ana Carolina, Gisele Goulart Tavares, Ruan Medina, Camila Martins Saporetti, Tiago Silveira Gontijo, Matteo Bodini, Leonardo Goliatt, and Priscila Capriles. 2025. "A Computational Approach for Identifying Keywords Related to the 2030 Agenda for Sustainable Development Goals in a Brazilian Higher Education Institution" Societies 15, no. 6: 165. https://doi.org/10.3390/soc15060165
APA StyleEstorani Polessa, A. C., Tavares, G. G., Medina, R., Saporetti, C. M., Gontijo, T. S., Bodini, M., Goliatt, L., & Capriles, P. (2025). A Computational Approach for Identifying Keywords Related to the 2030 Agenda for Sustainable Development Goals in a Brazilian Higher Education Institution. Societies, 15(6), 165. https://doi.org/10.3390/soc15060165