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By relying on the Rank-1 metric, we assign each document exactly one main topic, namely the topic that is most prevalent in this document according to the document-topic-matrix. This will depend on how you want the LDA to read your words. These are topics that seem incoherent and cannot be meaningfully interpreted or labeled because, for example, they do not describe a single event or issue. For. We see that sorting topics by the Rank-1 method places topics with rather specific thematic coherences in upper ranks of the list. Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Hussler, T., Schmid-Petri, H., & Adam, S. (2018). Here is the code and it works without errors. For this, I used t-Distributed Stochastic Neighbor Embedding (or t-SNE). As before, we load the corpus from a .csv file containing (at minimum) a column containing unique IDs for each observation and a column containing the actual text. This interactive Jupyter notebook allows you to execute code yourself and you can also change and edit the notebook, e.g. There is already an entire book on tidytext though, which is incredibly helpful and also free, available here. This is really just a fancy version of the toy maximum-likelihood problems youve done in your stats class: whereas there you were given a numerical dataset and asked something like assuming this data was generated by a normal distribution, what are the most likely \(\mu\) and \(\sigma\) parameters of that distribution?, now youre given a textual dataset (which is not a meaningful difference, since you immediately transform the textual data to numeric data) and asked what are the most likely Dirichlet priors and probability distributions that generated this data?. STM also allows you to explicitly model which variables influence the prevalence of topics. Before turning to the code below, please install the packages by running the code below this paragraph. For the next steps, we want to give the topics more descriptive names than just numbers. Finally here comes the fun part! NLP with R part 1: Identifying topics in restaurant reviews with topic modeling NLP with R part 2: Training word embedding models and visualizing the result NLP with R part 3: Predicting the next . The tutorial by Andreas Niekler and Gregor Wiedemann is more thorough, goes into more detail than this tutorial, and covers many more very useful text mining methods. If it takes too long, reduce the vocabulary in the DTM by increasing the minimum frequency in the previous step.