Text data is inherently temporal. The meaning of words and phrases changes over time, and the context in which they are used is constantly evolving. This is not just true for social media data, where the language used is rapidly influenced by current events, memes and trends, but also for journalistic, economic or political text data. Most NLP techniques however consider the corpus at hand to be homogenous in regard to time. This is a simplification that can lead to biased results, as the meaning of words and phrases can change over time. For instance, running a classic Latent Dirichlet Allocation on a corpus that spans several years is not enough to capture changes in the topics over time, but only portraits an "average" topic distribution over the whole time span. Researchers have developed a number of tools for analyzing text data over time. However, these tools are often scattered across different packages and libraries, making it difficult for researchers to use them in a consistent and reproducible way. The ttta package is supposed to serve as a collection of tools for analyzing text data over time.
View on arXiv@article{lange2025_2503.02625,
title={ ttta: Tools for Temporal Text Analysis },
author={ Kai-Robin Lange and Niklas Benner and Lars Grönberg and Aymane Hachcham and Imene Kolli and Jonas Rieger and Carsten Jentsch },
journal={arXiv preprint arXiv:2503.02625},
year={ 2025 }
}