Introduction to the tidylda package | Tidy Methods | Other Notable Features | Discussion | Installation | Example

Fine Tuning Latent Dirichlet Allocation for Transfer Learning | Contribution | tLDA | The Model | Selecting the prior weight | The tidylda Implementation of tLDA | Aligning Vocabulary | Adding New Topics | Initializing $\boldsymbol{Cd}^{(t)}$ and $\boldsymbol{Cv}^{(t)}$ | Partitioning the Posterior to Collapse $K$ Weights ($\omega_k$) Into One, $a$

Why textmineR? | Corpus management | Creating a DTM | Basic corpus statistics

Document clustering

Topic modeling | LDA Example | LSA Example | Other topic models | Extensions | Document clustering is just a special topic model | Choosing the number of topics | Using topic models from other packages

Text embeddings | Create a term co-occurrence matrix | Fitting a model | Interpretation of $\Phi$ and $\Theta$ | Evaluating the model | Embedding documents under the model | Where to next?

Getting started | Building a basic document summarizer | Pulling it all together

Using tidytext with textmineR

Introduction | Traditional R-squared | Multivariate R-squared | Multivariate prediction | A silly example to prove a point | A more realistic example | Probabilistic topic modeling | Latent semantic analysis topic modeling | Parallelization/distributed computing for large data sets