knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )

TagR is SHARE's R package for topic tagging using machine learning. Using predefined topics and a labelled data set, it allows the user to find the probability that an unlabelled post belongs to a particular topic.

This package is used by running through the following steps:

- Pre-process the text data
- Conduct exploratory data analysis
- Create document-term matrices
- Bind any numerical variables to the document-term matrices
- Find an optimal set of parameters for the Xgboost model for each topic
- Train a model for each topic and predict the probabilities that each post in the unlabelled data set belongs to such topic
- Build an explainer that helps the user visualise how the Xgboost model makes its predictions

You can install the released version of TagR from CRAN with:

This is a basic example which shows you how to solve a common problem:

library(TagR) ## basic example code

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