Tracing Topic Transitions with Temporal Graph Clusters

作者

  • Xiaonan Jing Purdue University
  • Qingyuan Hu Purdue University
  • Yi Zhang Purdue University
  • Julia Taylor Rayz Purdue University

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https://doi.org/10.32473/flairs.v34i1.128547

关键词:

topic transition, graph-of-words, graph clustering, Twitter, natural language processing

摘要

Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.

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已出版

2021-04-18

##submission.howToCite##

Jing, X., Hu, Q., Zhang, Y., & Rayz, J. T. (2021). Tracing Topic Transitions with Temporal Graph Clusters. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128547

期

卷 34 (2021): Proceedings of FLAIRS-34

栏目

Main Track Proceedings