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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 10 | Oct 2025

p-ISSN: 2395-0072

www.irjet.net

Sentiment-Aware Stakeholder Engagement in Projects: A Conceptual Framework Madhusudan Bangalore Nagaraja1 1Technical Delivery Manager, Esystems-Inc & Dallas, TX, USA

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Abstract - Effective stakeholder management is critical to

predominantly reactive, identifying issues only after they have escalated into significant problems. Second, they rely heavily on subjective assessments and periodic surveys that may not capture real-time sentiment fluctuations. Third, the volume of digital communication in modern projects creates an information processing challenge that exceeds human analytical capabilities. Finally, existing approaches lack systematic integration between qualitative stakeholder feedback and quantitative project performance metrics.

project success, yet traditional approaches rely on periodic assessments and subjective evaluations that often miss early warning signs of stakeholder disengagement. This paper presents a conceptual framework for sentiment-aware stakeholder management that integrates artificial intelligence-powered sentiment analysis with established stakeholder theory and project management practices. The framework combines natural language processing techniques with traditional project metrics to create predictive models for three key outcomes: Risk of Scope Creep (binary classification), Likelihood of Project Delay (probability estimation), and Stakeholder Satisfaction Score (regression analysis). By systematically analyzing communications from multiple channels—emails, meeting transcripts, chat messages, and social media posts related to the project, the framework enables proactive stakeholder engagement through early detection of sentiment-related risks. The proposed approach transforms qualitative stakeholder feedback into quantifiable metrics, providing project managers with data-driven insights for intervention strategies. This conceptual framework contributes to the project management literature by bridging sentiment analysis technology with stakeholder theory, offering a systematic approach to enhance project outcomes through improved stakeholder relationships.

1.2 Research Objectives This research aims to develop a comprehensive conceptual framework that addresses these limitations by leveraging artificial intelligence to create sentiment-aware stakeholder management capabilities. The specific objectives are: 1.

Theoretical Integration: Establish a conceptual foundation that bridges stakeholder theory with sentiment analysis technology

Framework Development: Design a systematic approach for transforming stakeholder communications into actionable insights Predictive Modeling: Define target variables and prediction models that link sentiment patterns to project outcomes Validation Strategy: Propose methods for evaluating the framework's effectiveness and practical utility

Key Words: Sentiment Analysis, Stakeholder Management, Project Management, Artificial Intelligence, Conceptual Framework, Natural Language Processing

1.INTRODUCTION 1.1 Background and Problem Statement

1.3 Contribution to Knowledge

Project management in the digital era faces unprecedented challenges in stakeholder communication and engagement. The Project Management Institute reports that ineffective communication contributes to project failure in 33% of cases, with organizations risking significant value— up to $75 million per $1 billion invested—due to communication breakdowns [1]. As projects become increasingly complex and stakeholder ecosystems more diverse, traditional approaches to stakeholder management prove inadequate for capturing the nuanced dynamics of stakeholder sentiment and engagement.

This work contributes to both theoretical and practical knowledge in several ways. Theoretically, it extends stakeholder theory by incorporating real-time sentiment dynamics and provides a sociotechnical perspective on stakeholder-project interactions. Methodologically, it introduces a novel integration of natural language processing with traditional project metrics for predictive modeling. Practically, it offers project managers a datadriven approach to stakeholder engagement that can improve project outcomes through proactive intervention strategies.

Current stakeholder management practices suffer from several fundamental limitations. First, they are

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