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There are several Goodness-of-Fit (GoF) metrics you can use to assess a LDA model. The most common is called perplexity which you can compute trough the function perplexity() in the package topicmodels. The way you select the optimal model is to look for a "knee" in the plot. The idea, stemming from unsupervised methods, is to run multiple LDA models with different topics. As the number of topics increases, you should see the perplexity decrease. You want to stop either when you find a knee or when the incremental decrease is negligible. Thin about the scree plot when you run the Principal Component Analysis.

Having said that, there is an R package called ldatuning which implements four additional metrics based on density-based clustering and on Kullback-Leibler divergence. Three of them can be used with both VEM and Gibbs inference, while the method by Griffith can only be used with Gibbs. For some of these metrics you look for the minimum, for other for the maximum. Also, you can always compute the log-likelihood of your model which want to maximize. The way you can extract the likelihood from an LDA object is pretty straightforward. Let's assume you have an LDA model called ldamodel:

If you consider neg_positive as negative, and neg_negative as positive, then you could create your index by combining the pairs of columns. This is plausible because the "neg positive" for instance contains sequences such as "not good".

The only "solution" I could find was to re-install RStudio.

Not entirely sure what the question is but if you want to clean the text in df and then convert it to a corpus, here would be the way to go: