International Journal on Science and Technology

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Integrating Sentiment Analysis and Topic Modeling for Social License to Operate

Author(s) Syed Arham Akheel, Tarun Shrivas
Country United States
Abstract Social License to Operate (SLO) refers to the informal acceptance or approval that organizations receive from their stakeholders and the broader community. In recent years, sentiment analysis and topic modeling have emerged as powerful tools for understanding the nuances of community perceptions in real time. This literature review examines the technical foundations of sentiment analysis and Latent Dirichlet Allocation (LDA)-based topic modeling, and explores how their hybrid integration can provide richer insights into stakeholder sentiments relevant to SLO. By analyzing scholarly works that investigate machine learning algorithms, natural language processing (NLP) techniques, and case studies across various industries, this paper synthesizes the current methodological landscape and identifies emerging trends and knowledge gaps. The findings underscore the importance of robust data governance, interdisciplinary collaboration, and ethical considerations for effectively deploying hybrid models to maintain and strengthen an organization’s Social License to Operate.
Keywords Social License to Operate, Sentiment Analysis, Topic Modeling, Latent Dirichlet Allocation, Hybrid Approaches, Natural Language Processing
Field Engineering
Published In Volume 12, Issue 2, April-June 2021
Published On 2021-06-08
Cite This Integrating Sentiment Analysis and Topic Modeling for Social License to Operate - Syed Arham Akheel, Tarun Shrivas - IJSAT Volume 12, Issue 2, April-June 2021. DOI 10.5281/zenodo.14866293
DOI https://doi.org/10.5281/zenodo.14866293
Short DOI https://doi.org/g84xm8
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