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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65646
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and Data Science
Anjuman-I-Islam's Abdul Razzaq Kalsekar Polytechnic
Artificial Intelligence and Data Science
Anjuman-I-Islam's Abdul Razzaq Kalsekar Polytechnic
Social media has become one of the most powerful platforms for digital marketing, enabling businesses to connect with customers, promote products, and build brand awareness. However, managing multiple social media platforms, creating engaging content, analyzing audience behavior, and evaluating campaign performance manually can be time-consuming and inefficient. To address these challenges, this research proposes an AI Marketing Agent with Social Media Analytics, an intelligent marketing platform that integrates Artificial Intelligence, Data Analytics, and Predictive Analytics to automate social media marketing activities. The proposed system provides secure user authentication, AI-powered content generation, caption generation, hashtag recommendation, and automated reply generation to simplify content creation. It also performs sentiment analysis, audience engagement analysis, competitor analysis, and trend detection to provide valuable insights into social media performance. Furthermore, the system predicts engagement levels, estimates post performance, and recommends the best time to publish content for maximum audience reach. A professional web dashboard was developed using Python, Streamlit, SQLite, Pandas, Matplotlib, and TextBlob, allowing users to visualize marketing metrics and generate automated marketing reports. The system also incorporates advanced AI capabilities such as Influencer Recommendation, Viral Content Prediction, Social Media Trend Forecasting, AI Marketing Strategy Generation, and Customer Segmentation to support intelligent business decision-making. Experimental evaluation using a sample social media dataset demonstrated that the proposed platform effectively automates marketing workflows, improves content planning, enhances audience engagement analysis, and assists businesses in making data-driven marketing decisions. The proposed AI Marketing Agent serves as a scalable and user-friendly solution for modern digital marketing applications.
KEYWORDS
The proposed AI Marketing Agent with Social Media Analytics successfully demonstrates how Artificial Intelligence, Natural Language Processing (NLP), Data Analytics, and Predictive Analytics can be integrated into a single intelligent platform for social media marketing. The system automates essential marketing activities, including user authentication, AI-based content generation, caption generation, hashtag recommendation, automated reply generation, sentiment analysis, audience engagement analysis, competitor analysis, trend detection, engagement prediction, and marketing report generation. The developed web-based dashboard provides an interactive and user-friendly interface for visualizing marketing performance, analyzing campaign effectiveness, and generating AI-driven insights. By automating repetitive marketing tasks and presenting meaningful analytics, the system reduces manual effort, improves marketing efficiency, and supports data-driven decision-making. Furthermore, the proposed framework is scalable and can be enhanced with real-time social media API integration, Google Authentication, advanced Machine Learning models, mobile application support, and cloud deployment. Overall, the AI Marketing Agent offers a modern, intelligent, and efficient solution for businesses, marketers, and content creators seeking to optimize their digital marketing strategies through Artificial Intelligence and Social Media Analytics.
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Publication Date
6/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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