Visualization of Topic Transitions in SNSs Using Document Embedding and Dimensionality Reduction.
Tiandong XiaoYosuke OnouePublished in: PacificVis (2021)
Keyphrases
- dimensionality reduction
- multidimensional scaling
- structure preserving
- nonlinear dimensionality reduction
- document content
- graph embedding
- latent topics
- document set
- topic discovery
- low dimensional
- topic hierarchy
- locality preserving projections
- high dimensional data
- kernel pca
- low dimensional spaces
- data representation
- principal component analysis
- social media
- high dimensionality
- textual content
- feature extraction
- embedding space
- document collections
- document images
- multi dimensional scaling
- keywords
- high dimensional
- information retrieval systems
- web documents
- manifold learning
- information retrieval
- topic models
- document clustering
- document retrieval
- multi document summarization
- scientific papers
- linear discriminant analysis
- document representation
- pattern recognition
- document corpus
- focused crawler
- single document summarization
- related documents
- latent space
- dimensionality reduction methods
- latent dirichlet allocation
- vector space
- data points
- automatic summarization
- blog posts
- cross document
- topic modeling
- principal components
- data analysis