A systematic review to identify the effects of tea by integrating an intelligence-based hybrid text mining and topic model.
You-Shyang ChenChing-Hsue ChengWei-Lun HungPublished in: Soft Comput. (2021)
Keyphrases
- topic models
- text mining
- systematic review
- topic modeling
- latent dirichlet allocation
- text documents
- latent topics
- information extraction
- information retrieval
- probabilistic model
- latent variables
- natural language processing
- co occurrence
- empirical studies
- data mining
- topic discovery
- generative model
- text classification
- knowledge discovery
- artificial intelligence
- variational inference
- document clustering
- gibbs sampling
- recommender systems
- network analysis
- baseline models
- computational linguistics
- text corpora
- text streams
- probabilistic topic models
- microblog posts
- probabilistic latent semantic analysis
- data analysis
- natural language
- machine learning