warplda | Cache efficient implementation for Latent Dirichlet | Search Engine library

I was wondering how results of different packages, hence, algorithms, differ and if parameters could be set in a way to produce similar topics. I had a look at the packages text2vec and topicmodels in particular.

I used below code to compare 10 topics (see code section for terms) generated with these packages. I could not manage to generate sets of topics with similar meaning. E.g. topic 10 from text2vec has something to do with "police", none of the topics produced by topicmodels refers to "police" or similar terms. Further, I could not identify a pendant of topic 5 produced by topicmodels that has something to do with "life-love-familiy-war" in the topics produced by text2vec.

I am a beginner with LDA, hence, my understanding may sound naive for experienced programmers. However, intuitively, one would asssume that it should be possible to produce sets of topics with similar meaning to prove validity/robustness of results. Of course, not necessarily the exact same set of terms, but termlists addressing similar topics.

Maybe the issue is simply that my human interpretation of these termlists is not good enough to capture similarities, but maybe there are some parameters that might increase similarity for human interpretation. Can someone guide me on how to set parameters to achieve this or otherwise provide explanations or hint on suitable resources to improve my understanding of the matter?

Here some issues that might be relevant:

• I know that text2vec does not use standard Gibbs sampling but WarpLDA, which already is a difference in the algorithm to topcimodels. If my understanding is correct, the priors alpha and delta used in topicmodels are set as doc_topic_prior and topic_word_prior in text2vec respectively.
• Furthermore, in postprocessing, text2vec allows the adaption of lambda for sorting terms of topics based on their frequency. I have not yet understood, how terms are sorted in topicmodels - comparable to setting lambda=1?. (I have tried different lambdas between 0 to 1 without getting similar topics)
• Another issue is that is seems difficult to produce a fully reproducible example even when setting seed (see, e.g., this question). This is not directly my question but might make it more difficult to respond.

Sorry for the lenghty question and thanks in advance for any help or suggestions.

Update2: I have moved the content of my first update into an answer that is based on a more complete analysis.

Update: Following the helpful comment of text2vec package creator Dmitriy Selivanov, I can confirm that setting lambda=1 increases the similarity of topics betweeen the termlists produced by the two packages.

Furthermore, I had a closer look at the differences between termlists produced by both packages via a quick check of length(setdiff()) and length(intersect()) across topics (see in below code). This rough check shows that text2vec discards several terms per topic - probably by a threshold of probability for the individual topics? topicmodels keeps all terms for all topics. This explains part of the differences in meanings that can be derived (by a human) from the termlists.

As mentioned above already, generating a reproducible example seems difficult, so I have not adapted all data examples in below code. Since run time is short, anybody can check on his/her own system.