An effective short-text topic modelling with neighbourhood assistance-driven NMF in Twitter
Bibliographic Data
| ID | 4694582 |
|---|---|
| Authors | Shalani Athukorala (0000-0001-8047-1264, University of Ruhuna, corresponding author), Wathsala Mohotti, Wathsala Anupama Mohotti (0000-0002-5720-7737, University of Ruhuna) |
| Year | 2022 |
| Volume | 12 |
| Issue | 1 |
| Pages | 89-89 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-022-00898-5 |
| PMID | 35911485 |
| OpenAlex | W4286823341 |
| Language | EN |
| Citations received | 3 |
| References cited | 38 |
Algorithm · Data mining · Dimensionality reduction · Information retrieval · Matrix decomposition · Non-negative matrix factorization · Social media · Topic model · World Wide Web · Advanced Text Analysis Techniques · Computational and Text Analysis Methods · Computer Science · Topic Modeling · Artificial Intelligence
Latent Dirichlet allocation (LDA) and topic modeling
A biterm topic model for short texts
Probabilistic latent semantic indexing
Learning the parts of objects by non-negative matrix factorization
Probabilistic topic models
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |