ARTIFICIAL INTELLIGENCE IN PUBLIC RELATIONS ANALYTICS: A FRAMEWORK FOR ENHANCED DECISION-MAKING AND STRATEGIC INSIGHTS
Keywords:
Artificial Intelligence Analytics, Public Relations Technology, Media Monitoring Automation, Sentiment Analysis, Strategic CommunicationsAbstract
This article examines the transformative impact of artificial intelligence on public relations analytics, focusing on its integration into media monitoring, sentiment analysis, and strategic decision-making processes. Through a comprehensive article analysis of current AI applications in PR, the article explores how natural language processing and machine learning algorithms enhance real-time media monitoring, automate routine tasks, and provide deeper insights into public sentiment. The article investigates the role of AI in social listening tools, highlighting improvements in conversation filtering, influencer identification, and crisis detection capabilities. Additionally, it addresses the professional implications of AI adoption, examining how these technologies empower PR practitioners while presenting new challenges in terms of skill development and workflow adaptation. The article also critically evaluates the ethical considerations surrounding AI implementation, including privacy concerns and algorithmic bias. Findings suggest that while AI significantly enhances analytical capabilities and operational efficiency in PR, human expertise remains crucial for strategic interpretation and contextual understanding. This article contributes to the growing body of knowledge on technological integration in public relations practice, offering insights for practitioners and researchers exploring the intersection of AI and strategic communications.
References
M. Abolghasemi and R. Esmaeilbeigi, "State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge," arXiv preprint arXiv:2112.03595, 2021. DOI: 10.48550/arXiv.2112.03595 Available: https://doi.org/10.48550/arXiv.2112.03595
A. Brem, F. Giones, and M. Werle, "The AI Digital Revolution in Innovation: A Conceptual Framework of Artificial Intelligence Technologies for the Management of Innovation," IEEE Transactions on Engineering Management, vol. 70, no. 2, p. 770, 2021. DOI: 10.1109/TEM.2021.3109983 Available: https://doi.org/10.1109/TEM.2021.3109983
I. Iliopoulos et al., "National press monitoring using Natural Language Processing as an early warning signal for prediction of asylum applications flows in Europe," 2022 IEEE International Conference on Big Data (Big Data), 2022. DOI: 10.1109/BigData55660.2022.10020278 Available: https://ieeexplore.ieee.org/abstract/document/10020278
A. N. Rosli et al., "Implementation of MQTT and LoRaWAN System for Real-time Environmental Monitoring Application," 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2020. DOI: 10.1109/ISCAIE47305.2020.9108808 Available: https://ieeexplore.ieee.org/abstract/document/9108808
T. U. Ahmed et al., "Facial expression recognition using convolutional neural network with data augmentation," 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2019, pp. 1-6. DOI: 10.1109/ICIEV.2019.8858529 Available: https://ieeexplore.ieee.org/document/8858529
M. Bouazizi and T. Ohtsuki, "A pattern-based approach for multi-class sentiment analysis in Twitter," IEEE Access, vol. 8, pp. 139872-139882, 2020. Available: https://www.researchgate.net/profile/Mondher-Bouazizi/publication/319197716_A_Pattern-Based_Approach_for_Multi-Class_Sentiment_Analysis_in_Twitter
I. Okpala, S. Halse, and J. Kropczynski, "Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis," 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022. Available: https://american-cse.org/csci2022-ieee/pdfs/CSCI2022-2lPzsUSRQukMlxf8K2x89I/202800a078/202800a078.pdf
S. F. Seyfosadat and R. Ravanmehr, "Systematic literature review on identifying influencers in social networks," Artificial Intelligence Review, vol. 56, pp. 567-660, 2023. Available: https://link.springer.com/article/10.1007/s10462-023-10515-2
J. S. Parker and R. L. Anderson, "Low-Energy Lunar Trajectory Design," Hoboken, NJ: John Wiley & Sons, Inc., 2014. Available: https://descanso.jpl.nasa.gov/monograph/series12/LunarTraj--Overall.pdf
R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015. Available: https://www.comp.nus.edu.sg/~reza/files/Shokri-CCS2015.pdf
N. Mehrabi et al., "A survey on bias and fairness in machine learning," ACM Computing Surveys (CSUR), 2021. Available: https://arxiv.org/abs/1908.09635
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