Modeling community structure and topics in dynamic text networks
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A network approach to topic models
One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical s…
Community DetectionModel SelectionSociologyStochastic Block Model+1Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code
Understanding source code is a topic of great interest in the software engineering community, since it can help programmers in various tasks such as software maintenance and reuse. Recent advances in large language model…
Code SearchLanguage ModelingLanguage ModellingLarge Language ModelBayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time…
Neural Topic Modeling with Large Language Models in the Loop
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in to…
Topic coverageTopic ModelsInter and Intra Topic Structure Learning with Word Embeddings
One important task of topic modeling for text analysis is interpretability. By discovering structured topics one is able to yield improved interpretability as well as modeling accuracy. In this paper, we propose a n…
Document ClassificationFormWord Embeddings