Modeling Fuzzy Cluster Transitions for Topic Tracing
This work addresses the problem of tracking dynamic topics in social media for NLP applications, but it appears incremental as it builds directly on prior research.
The authors tackled the challenge of tracing real-time topic evolution in Twitter data streams by proposing a framework that models fuzzy transitions of topic clusters, extending previous crisp cluster methods with fuzzy logic to enrich identified structures. They applied this to computer-generated clusters and human annotations, comparing fuzzy and crisp transitions on both datasets.
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.