Interpretation of topic modeling results between LDA and BERTopic
I’ve been utilizing BERTopic to output some results of my own corpus, but I am unsure whether the results (the output of the Python code) can be interpreted the same way as that produced by the traditional method which is LDA.I know that the two methods have different mathematical/computational mechanisms in terms of operation, but their outputs pretty much seem to be the same. So I was wondering if it is right to interpret the results of BERTopic the same way as I would interpret the results of LDA implementations.The reason I am asking this question is because I haven’t been able to find papers or tutorials specifically explaining about BERTopic outputs and how the interpretation of its outputs is similar/different to LDA. Also, papers that utilize BERTopic for research is very scarce, making it difficult to study it.Can someone provide some answers on this matter? If there is a link I can refer to or study regarding my question, as well as specific humanities/social science research examples that use BERTopic and compare it to LDA, that would also be greatly appreciated.FYI, I am working incomputational social scienceand trying to use BERTopic for my research, if this helps.