Topic modeling, an amalgam of ideas drawn from computer science, mathematics, and cognitive science, is evolving rapidly to help users understand and navigate huge stores of unstructured data. Topic ...

Abstract: Topic models are widely used unsupervised models capable of learning topics – weighted lists of words and documents – from large collections of text documents. When topic models are used for ...

This repository contains the code for our paper Neural Multimodal Topic Modeling: A Comprehensive Evaluation, presented at LREC-COLING 2024. Neural topic models can successfully find coherent and ...

A behind-the-scenes blog about research methods at Pew Research Center. For our latest findings, visit pewresearch.org. If you’ve ever had to analyze a set of documents — such as social media posts, ...

In this topic modelling analysis, we explore the application of topic modelling techniques to uncover latent themes within a corpus of tweets related to COVID-19. Social media platforms, especially ...

Recently topic models have emerged as a powerful tool to analyze document collections in an unsupervised fashion. The seminal work by Blei. et al. [1], starts by assuming that each document is a ...

Abstract: In recent years, deep compositional models have emerged as a popular technique for representation learning of sentence in computational linguistic and natural language processing. These ...