#> $ IC_NAME : chr "NATIONAL INSTITUTE ON DRUG ABUSE" "NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES" "NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES" "NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES". #> $ FUNDING_MECHANISM : chr "Research Centers" "Research Projects" "Research Projects" "Other Research Related". #> $ ED_INST_TYPE : chr "" "" "SCHOOLS OF MEDICINE" "SCHOOLS OF MEDICINE". #> $ ADMINISTERING_IC : chr "DA" "GM" "AI" "AI". #> $ ABSTRACT_TEXT : chr "Methamphetamine (MA) is remarkably addictive and relapse to excessive use is highly probable and poses serious "| _truncated_ " Project Summary Risk bimarkers have become increasingly important in clinical decision making, guiding patient"| _truncated_ " DESCRIPTION (provided by applicant): Despite enormous efforts, no effective vaccine is currently available "| _truncated_ " DESCRIPTION (provided by applicant): This four-year K01 award application is to provide intensive multi-dis"| _truncated_. Library(textmineR) # load nih_sample data set from textmineR data(nih_sample) str(nih_sample) #> 'ame': 100 obs. Examples include an R-squared for probabilistic topic models ( working paper here), probabilistic coherence (a measure of topic quality), and a topic labeling function based on most-probable bigrams. In addition, textmineR has utility functions for topic models. With LSA, for example, there is a third object representing the singular values in the decomposition. For non-probabilistic models (e.g. LSA) these distributions are, obviously, not probabilities. In the case of probabilistic models, these are categorical probability distributions. The second, phi ( \(\Phi\)), has rows representing a distribution of words over topics. The first, “theta” ( \(\Theta\)), has rows representing a distribution of topics over documents. TextmineR’s consistent representation of topic models boils down to two matrices. (And textmineR takes advantage of the RSpectra package for LSA’s single-value decomposition.) Plans exist to implement LDA natively with Rcpp sometime in 2018. (Examples with LDA and LSA follow below.) As of this writing, textmineR’s LDA and CTM functions are wrappers for other packages to facilitate a consistent workflow. You can fit Latent Dirichlet Allocation (LDA), Correlated Topic Models (CTM), and Latent Semantic Analysis (LSA) from within textmineR. TextmineR has extensive functionality for topic modeling.
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