Purpose Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These...

Gender stereotypes pose an important hurdle on the way to gender equality. It is difficult to quantify the problem, though, as stereotypical beliefs are often subconscious or not openly expressed. User-generated content (UGC) opens up novel opportunities to overcome such challenges, as the...

Construimos una base de datos de indicadores de incertidumbre de política económica (economic policy indicators: EPU, por sus siglas en inglés) de acceso público basada en la metodología propuesta por Azqueta-Gavaldón, Hirschbühl, Onorante y Saiz (2023), que utiliza técnicas de topic...

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right...

Fiscal policy is made in parliament. We go to the roots of changes of fiscal policy in Germany and use a novel data set on all parliamentary speeches in the Bundestag from 1960 to 2021. We propose an embedding-based approach, which allows the representation of words and documents in a shared...

Fiscal policy is made in parliament. We go to the roots of changes of fiscal policy in Germany and use a novel data set on all parliamentary speeches in the Bundestag from 1960 to 2021. We propose an embedding-based approach, which allows the representation of words and documents in a shared...