ttta: Tools for Temporal Text Analysis

2025-03-04

AI Summary

Contemporary NLP methods often treat text as temporally homogeneous, neglecting semantic evolution over time and thereby introducing temporal semantic bias; moreover, existing tools for temporal text analysis are fragmented and lack reproducibility. To address these limitations, we propose the first unified framework that systematically integrates multi-granular temporal text analysis, enabling both topic evolution modeling and lexical semantic shift detection. Implemented in Python, the framework incorporates dynamic topic modeling, temporal word embedding alignment, sliding-window LDA, and interactive visualization modules. It ensures end-to-end reproducibility and achieves significant improvements in topic trend identification accuracy on cross-year news and social media datasets. Our core contribution is the first standardized, open-source, and extensible toolkit for temporal NLP—bridging the gap between theoretical models of language evolution and empirical, large-scale diachronic analysis.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Analyzing temporal changes in text data meaning.
• Addressing bias from time-homogeneous NLP techniques.
• Providing unified tools for temporal text analysis.

Innovation

• Temporal text analysis tools integration
• Dynamic word meaning tracking over time
• Unified package for time-aware NLP