<p>The R code for this tutorial on Methods of Distributional Semantics in R is found in the respective<a href="https://github.com/GoranMilovanovic/Distributional-Semantics-in-R"> GitHub repository</a>. <br></p><p>Following my<a href="http://www.exactness.net/post/153902588705/belgrader-meetup-methods-of-distributional"> Methods of Distributional Semantics in R BelgradeR Meetup</a> with<a href="http://www.datascience.rs/"> Data Science Serbia</a>, organized in <a href="http://en.startit.rs/">Startit Center</a>, Belgrade, 11/30/2016, several people asked me for the R code used for the analysis
of William Shakespeare’s plays that was presented. I have decided to continue the development of the code that I’ve used during the Meetup in order to advance the examples that I have shown then into a more or less complete and comprehensible text-mining tutorial with<a href="https://cran.r-project.org/web/packages/tm/index.html"> {tm}</a>,<a href="https://cran.r-project.org/web/packages/openNLP/index.html"> {openNLP}</a>, and<a href="https://cran.r-project.org/web/packages/topicmodels/index.html"> {topicmodels}</a> in R. All files in this <a href="https://github.com/GoranMilovanovic/Distributional-Semantics-in-R">GitHub</a> repository are a product of that work.
</p><p>The idea here is to provide an overview of selected R packages and functions for text-mining and modeling in distributional semantics. Instead of presenting functions and packages in a piece-wise fashion, I have decided to develop a full text-mining pipeline by combining the essential steps orderly and exactly as one would need to follow them to arrive at some useful Data Science production following data wrangling, checking for integrity, text pre-processing, and modeling.</p><p>The first notebook - <a href="https://github.com/GoranMilovanovic/Distributional-Semantics-in-R/blob/master/DistSemanticsBelgradeR.Rmd">Part 1: The {tm} structures for text-mining in R</a> - introduces the classes provided by the {tm} package, and show you how to index a text corpus with metadata prior to modeling and analytics. I have also introduced the essential read and write (i.e. Vcorpus formation)  operations from {tm} there. <br></p><p>The forthcoming Part 2. of this tutorial will cover Entitity Recognition with {OpenNLP}. We will check how well can machine learning tell what characters appear in which Shakespeare’s play. In Part 3. we will deal with text pre-processing with {tm}, while Part 4. introduces topic modeling with Latent Dirichlet Allocation. Part 5, finally, will present an analytical exploration of the topic model.<br></p><p><i>A semantic network of Shakespeare’s characters produced by {igraph} and from a previously developed LDA model from {topicmodels}.</i><br></p><p>The video of the Meetup that motivated me to develop this tutorial is on <a href="https://www.youtube.com/watch?v=lCt4GNHGZkE">YouTube</a> - however, no English titles yet.<br></p><p>The exercise uses the complete plays of <a href="https://en.wikipedia.org/wiki/William_Shakespeare">William Shakespeare</a> kindly provided by the <a href="http://web.mit.edu/">Massachusetts Institute of Technology</a> at their <a href="http://shakespeare.mit.edu/">The Complete Works of William Shakespeare</a> pages.</p><p>Stay tuned for more text-mining in R. <br></p>