A clustering-based topic model using word networks and word embeddings

HIGHLIGHTS

• who: Wenchuan Mu from the University have published the research work: A clustering-based topic model using word networks and word embeddings, in the Journal: (JOURNAL)
• what: To address this challenge the authors develop the Clustering-based Topic Modelling (ClusTop) algorithm that first constructs different types of word networks based on different types of n-grams co-occurrence and word embedding distances. Using three Twitter datasets with labelled crises and events as topics the authors show that ClusTop outperforms various traditional baselines in terms of topic coherence pointwise mutual information precision recall and F-score . . .
  

   

  

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