Enhanced AI Model for Multimodal Influencer Marketing Analysis and Classification

Social media platforms have become a hub for influencer marketing, where understanding influencer categories and post content is crucial. This study presents an Enhanced Influencer Profiler model that leverages both text and image data for classifying influencers and posts with exceptional accuracy. The study utilizes an enhanced multimodal encoder, combining Efficient Net for image feature extraction with BERT, RoBERTa, and GPT-3 for textual features, and apply a cross-attention mechanism to fuse these features into a unified representation. In order to extend the definitional aspect of the influencer representation, the study considers a multi-level attention framework that can analyses internal and external interactions between influencers as well as their temporal and engagement patterns. In the context of influencer classification, the authors obtain a remarkable accuracy measurement of 99.62 % and this model maintains high scores of F1 in each category to quantify the effectiveness of this classification model. In post classification, the low-level model attains a level of 98.45% this is much higher than the general baseline method like SVC (71.60%) and Random Forest (76.25%). The findings described here demonstrate the effectiveness and flexibility of the model in multimodal classification tasks and outperform traditional models. Thus, the Extended Influencer Profiler, which has been developed using the Python software for data processing and training, shows the feasibility of the method. Therefore, the research findings propose that this model can be applied for influencer marketing and content classification in order to work more accurately and efficiently for marketers and analysts. In general, the concept of the Enhanced Influencer Profiler contributes to the progress of social media analysis practices, especially for dealing with multiple and heterogeneous data. Possible research directions for future work may involve imposing other modalities and evaluating performance and real-time data analysis for even more diverse fields of use.