# Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index

arXiv: https://arxiv.org/abs/2111.00057 · HTML: https://mlsanity.com/paper/2111.00057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Language Modeling by Clustering with Word Embeddings for Text Readability Assessment](https://mlsanity.com/paper/1709.01888/) (2017)
- [Topic2Vec: Learning Distributed Representations of Topics](https://mlsanity.com/paper/1506.08422/) (2015)
- [Event detection in Colombian security Twitter news using fine-grained latent topic analysis](https://mlsanity.com/paper/1911.08370/) (2019)
- [Characterization of citizens using word2vec and latent topic analysis in a large set of tweets](https://mlsanity.com/paper/1904.08926/) (2019)
- [Using machine learning and information visualisation for discovering latent topics in Twitter news](https://mlsanity.com/paper/1910.09114/) (2019)

## Built on by (most cited first, 2 of 2)

- [Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning](https://mlsanity.com/paper/2602.12120/) (2026)
- [Beyond Keywords: Leveraging Generative LLMs and Label Aggregation to Classify Economic Policy Uncertainty in News Articles](https://mlsanity.com/paper/2609.35856/) (2026)

## Abstract (arXiv)

Quantification of economic uncertainty is a key concept for the prediction of macro economic variables such as gross domestic product (GDP), and it becomes particularly relevant on real-time or short-time predictions methodologies, such as nowcasting, where it is required a large amount of time series data, commonly with different structures and frequencies. Most of the data comes from the official agencies statistics and non-public institutions, however, relying our estimates in just the traditional data mentioned before, have some disadvantages. One of them is that economic uncertainty could not be represented or measured in a proper way based solely in financial or macroeconomic data, another one, is that they are susceptible to lack of information due to extraordinary events, such as the current COVID-19 pandemic. For these reasons, it is very common nowadays to use some non-traditional data from different sources, such as social networks or digital newspapers, in addition to the traditional data from official sources. The economic policy uncertainty (EPU) index, is the most used newspaper-based indicator to quantify the uncertainty, and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index, which incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing to update the index in real-time, which is a drawback with another proposals that use computationally intensive methods for topic modeling, such as Latent Dirichlet Allocation (LDA). We show that our proposal allow us to update the index and significantly reduces the time required for new document assignation into topics.