# A Computational Framework to Identify Self-Aspects in Text

arXiv: https://arxiv.org/abs/2507.13115 · HTML: https://mlsanity.com/paper/2507.13115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 115 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 25 of 25)

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- [Can Large Language Models Identify Authorship?](https://mlsanity.com/paper/2403.08213/) (2024)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Intrinsic User-Centric Interpretability through Global Mixture of Experts](https://mlsanity.com/paper/2402.02933/) (2024)
- [Zero and Few-shot Learning for Author Profiling](https://mlsanity.com/paper/2204.10543/) (2022)
- [Lemotif: An Affective Visual Journal Using Deep Neural Networks](https://mlsanity.com/paper/1903.07766/) (2019)

## Abstract (arXiv)

This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-based analysis. In line with this, we plan to introduce an ontology of Self-aspects and a gold-standard annotated dataset. Using this foundation, we will develop and evaluate conventional discriminative models, generative large language models, and embedding-based retrieval approaches against four main criteria: interpretability, ground-truth adherence, accuracy, and computational efficiency. Top-performing models will be applied in case studies in mental health and empirical phenomenology.