Profile-Level Positivity Assessment Using Multimodal Feature Aggregation From Social Media Content

Accurately evaluating positivity in social media profiles has important implications for advertising, mental health monitoring, and responsible AI applications. However, the multimodal nature of Instagram, where images, captions, hashtags, and emojis co-occur, challenges conventional unimodal sentiment analysis tools. We present a multimodal system that integrates computer vision and natural language processing to assess Instagram profiles. The architecture comprises two blocks: (i) feature extraction via facial-emotion analysis, object detection, chromatic cues, and linguistic processing; and (ii) profile-level inference, which fuses aggregated features to produce an overall positivity score together with semantically meaningful profile tags. Experiments on public Instagram profiles (images, captions, and comments), including an expert-annotated subset of comments, compare unimodal baselines with transformer-based embeddings and OpenAI large language models. The multimodal pipeline improves over unimodal variants, and the OpenAI approach achieves higher concordance with expert judgements than RoBERTa/Bi-LSTM baselines. Beyond technical contributions, we discuss privacy, transparency, and misuse risks associated with affective computing on social media. The proposed framework contributes to the advancement of multimodal sentiment analysis in social media and highlights its applicability to areas such as marketing analytics, well-being monitoring, and ethically aligned AI systems.