A self structuring artificial intelligence framework for deep emotions modeling and analysis on the social web

摘要

The social web has enabled individuals from all walks of life to openly express their emotions and sentiment in relation to current affairs, local issues and personal circumstances. Within the social web, social media encompasses deep emotional expressions that reflect a multitude of personalities and behaviors. Existing research in this space is heavily focused on supervised sentiment analysis and emotion detection, with limited work on modeling these deep emotions, mixed emotions and variations of emotional behaviors from unlabeled and unstructured social media conversations. In this study, we propose a comprehensive framework based on the principles of self-structuring artificial intelligence for emotion modeling and analysis that systematically integrates the modeling capabilities at a granular level on unstructured, unlabeled social media data. The research contributions of this framework are the detection, analysis and synthesis of deep emotion intensity, emotion transitions, emotion latent representations, and profile-based emotion classification. The self-structuring artificial intelligence framework amalgamates an ensemble of novel algorithms to eventuate these contributions. These algorithms extend the current state-of-the-art of natural language processing techniques, word embedding, Markov chains and growing self-organizing maps, specifically for deep emotions modeling and analysis. The framework is empirically evaluated on anonymized conversations from online mental health support forums. The outcomes identify profile-based emotion characteristics, emotion intensities, transitions and an overall latent representation across three distinct mental health groups in these forums. These outcomes are comprehensive in comparison to existing work which singularly focuses on sentiment analysis or emotion detection. The validity and effectiveness of its application on a real-world social media setting further establish the methodological novelty of this ensemble of self-structuring artificial intelligence for deep emotions.