Modeling Fuzzy Cluster Transitions for Topic Tracing

TL;DR

This paper introduces a fuzzy logic-based framework to model and analyze real-time topic evolution in Twitter data, improving upon previous crisp cluster transition methods.

Contribution

It extends prior work by incorporating fuzzy logic into cluster transition modeling, enhancing the understanding of topic dynamics in streaming data.

Findings

Fuzzy transitions provide richer insights into topic evolution.

Comparison shows fuzzy approach outperforms crisp transitions.

Validated on both synthetic and human-annotated data.

Abstract

Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks. The continuously updating data streams on Twitter make it challenging to trace real-time topic evolution. In this paper, we propose a framework for modeling fuzzy transitions of topic clusters. We extend our previous work on crisp cluster transitions by incorporating fuzzy logic in order to enrich the underlying structures identified by the framework. We apply the methodology to both computer generated clusters of nouns from tweets and human tweet annotations. The obtained fuzzy transitions are compared with the crisp transitions, on both computer generated clusters and human labeled topic sets.