WETM: A word embedding-based topic model with modified collapsed Gibbs sampling for short text

Junaid Rashid, Jungeun Kim, Amir Hussain, Usman Naseem

Abstract: Highlights•A word embedding-based topic model (WETM) for short text documents.•Sparsity problem removed in short text and discovered structural information for topics and words.•A modified collapsed Gibbs sampling algorithm to find the parameters for WETM.•WETM achieved better classification, topic coherence, topic quality, and clustering results.•The execution time is lower for WETM as compared to baseline topic models.