Developing a Hybrid AI framework for predictive analytics on social media data

Yella, Anusha (2025) Developing a Hybrid AI framework for predictive analytics on social media data. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 2029-2037. ISSN 2582-8266

Well, with the rise of social networks comes a massive amount of data from which businesses can extract insight that can help them to steer their business decisions. Nonetheless, the sheer magnitude and unstructured nature of social media data pose a challenge to extracting tiered insights from it. So, the trend is moving towards predictive analytics, which is an analysis that uses statistical techniques on the data collected about previous behaviors to understand trends. Predictive analytics also helps make predictors and predict things in the future. Results. The proposed hybrid AI framework provides a substantial advantage in the identification of olfaction-induced emotional content from social media data. This allows us to discern more of the data about the dimensionality of olfactory perception. For example, you can apply ML to mining social media data to detect user behavior and sentiment trends. It helps businesses anticipate customer needs, tailor marketing methods, and improve customer engagement. Sentience cannot view videos or read complex language on social media, which is one of the most complicated types of data for machine learning. NLP methods such as sentiment analysis, topic modeling, and entity recognition can help generate insights from text-based social media data. By incorporating NLP with machine learning techniques, the hybrid AI framework enables capturing all varieties of social media data (including text, images, and videos) to make better predictions. The behavioral framework can be updated when new behavioral trends and patterns emerge as the social group data continues to grow and change. This ever-expanding approach ensures that the predictive analytics of the framework will be accurate to what is true of reality. Such hybrid AI frameworks can thus be implemented for a wide range of categories. Used for market research, brand cytometer, crisis detection, and targeted ads. By analyzing social media data, businesses can gain insights into the preferences, interests, and behavior patterns of their target market, enabling them to make informed decisions and stay current in the fast-paced market. We present a hybrid AI framework to address the challenges of using social media data for predictive analytics by combining the strengths of machine learning and NLP.".