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.

社交网络使各行各业的人可以公开表达自己对时事，当地问题和个人情况的情感和情感。在社交网络中，社交媒体包含了反映多种个性和行为的深刻情感表达。该领域中的现有研究主要集中在有监督的情绪分析和情感检测上，而对这些深层情感，混合情感以及来自无标签和无结构化社交媒体对话的情感行为变化的建模工作很少。在这项研究中，我们提出了一个基于自我构建人工智能原理的综合框架，用于情感建模和分析，该框架在非结构化，非结构化，未标记的社交媒体数据。该框架的研究贡献是对深度情感强度，情感转换，情感潜在表示以及基于配置文件的情感分类的检测，分析和综合。

自构造的人工智能框架将一系列新颖的算法融合在一起，以最终做出这些贡献。这些算法扩展了自然语言处理技术，单词嵌入，马尔可夫链和不断发展的自组织图谱的最新技术水平，特别是用于深度情感建模和分析。

根据在线精神卫生支持论坛的匿名对话对框架进行了经验评估。结果确定了这些论坛中三个不同的心理健康群体的基于轮廓的情绪特征，情绪强度，过渡和潜在的整体表现。与仅关注情绪分析或情绪检测的现有工作相比，这些结果是全面的。其在现实世界社交媒体上的应用的有效性和有效性进一步确立了这种针对深度情感的自构造人工智能合奏的方法学新颖性。