Inteligência artificial com processamento de linguagem natural em people analytics para predição de clima organizacional

• Ano de defesa: 2025
• Autor(a) principal: Vasconcellos, Sabrina da Silva
• Orientador(a): Belan, Peterson Adriano
• Banca de defesa: Belan, Peterson Adriano , Souza, Edson Melo de , Martins, Fellipe Silva , Alves, Wonder Alexandre Luz , Librantz, Andre Felipe Henriques
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação em Informática e Gestão do Conhecimento
• Departamento: Informática
• País: Brasil
• Palavras-chave em Português: análise de pessoas; inteligência artificial; processamento de linguagem natural; análise de sentimentos; clima organizacional
• Palavras-chave em Inglês: people analytics; artificial intelligence; natural language processing; sentiment analysis; organizational climate
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/3920

Context: The growing adoption of artificial intelligence (AI) technologies in organizations has driven significant advances in how human behavior in the workplace is analyzed. Although areas such as Human Resources (HR) collect large volumes of natural language data such as exit interviews, feedback, climate surveys, and eNPS (Employee Net Promoter Score) these data are still not fully leveraged in people analytics. Objective: To apply natural language processing (NLP) techniques to develop predictive methods for organizational climate, using exit interviews as the textual basis. Method: To this end, a supervised sentiment analysis model based on Random Forest was conducted, with and without the use of synthetic data. The direct scale dimensions (1–5), (0–10) and sentiment labels (detractor, neutral, and promoter) were manually defined by three HR specialists (E1, E2, and E3) and consolidated through a consensus model (E4). One of the specialists was from the studied organization, while the others came from different organizations, broadening the market perspective. Indirect eNPS dimensions were also considered, manually defined by specialist E2, including leadership, career, communication, diversity, health and well-being, retention, teams, training, innovation, and engagement, and associated with the questions and responses from the interviews. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, the study incorporated sentiment analyses grouped by indirect dimensions, applied statistics, visualizations, and temporal analyses, enabling observation of organizational climate evolution over time. Results: The final Random Forest model, trained exclusively on real data, achieved the best performance, reaching an accuracy of 75%. Conclusion: This result demonstrates the potential of the proposed approach to support data-driven strategic decisions, guide more targeted interventions, and contribute to more precise and human-centered people management in organizations.