# Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond

arXiv: https://arxiv.org/abs/1803.11045 · HTML: https://mlsanity.com/paper/1803.11045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)

## Abstract (arXiv)

Topic models are a family of statistical-based algorithms to summarize, explore and index large collections of text documents. After a decade of research led by computer scientists, topic models have spread to social science as a new generation of data-driven social scientists have searched for tools to explore large collections of unstructured text. Recently, social scientists have contributed to topic model literature with developments in causal inference and tools for handling the problem of multi-modality. In this paper, I provide a literature review on the evolution of topic modeling including extensions for document covariates, methods for evaluation and interpretation, and advances in interactive visualizations along with each aspect's relevance and application for social science research.