We propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics.
Apr 30, 2023 · Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic ...
Dec 9, 2024 · Topic model and word embedding reflect two perspectives of text semantics. Topic model maps documents into topic distribution space by utilizing ...
Apr 30, 2023 · Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic ...
Sep 27, 2016 · This paper studies how to incorporate the external word correlation knowledge into short texts to improve the coherence of topic modeling.
To address these issues, we propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify ...
Topic modeling is such a statistical framework that infers the latent and underlying topics from text documents, corpus, or electronic archives through a ...
This study proposes the Structural Topic Model (STM)-led framework to facilitate the interpretation of topic modeling results and standardize text analysis.
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A novel model for short text topic modeling, referred as Conditional Random Field regularized Topic Model (CRFTM), which not only develops a generalized ...
Using Topic Modeling Methods for Short-Text Data: A Comparative ...
pmc.ncbi.nlm.nih.gov › PMC7861298
We examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting ...