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A regular die has 6 sides, so the branching factor of the die is 6. This makes sense, because the more topics we have, the more information we have. But it has limitations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version . This can be seen with the following graph in the paper: In essense, since perplexity is equivalent to the inverse of the geometric mean, a lower perplexity implies data is more likely. As a rule of thumb for a good LDA model, the perplexity score should be low while coherence should be high. The idea is to train a topic model using the training set and then test the model on a test set that contains previously unseen documents (ie. The second approach does take this into account but is much more time consuming: we can develop tasks for people to do that can give us an idea of how coherent topics are in human interpretation. Why do small African island nations perform better than African continental nations, considering democracy and human development? In a good model with perplexity between 20 and 60, log perplexity would be between 4.3 and 5.9. After all, there is no singular idea of what a topic even is is. We are also often interested in the probability that our model assigns to a full sentence W made of the sequence of words (w_1,w_2,,w_N). In the literature, this is called kappa. Evaluate Topic Models: Latent Dirichlet Allocation (LDA) Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. As a rule of thumb for a good LDA model, the perplexity score should be low while coherence should be high. Kanika Negi - Associate Developer - Morgan Stanley | LinkedIn However, its worth noting that datasets can have varying numbers of sentences, and sentences can have varying numbers of words. Ideally, wed like to capture this information in a single metric that can be maximized, and compared. Evaluation helps you assess how relevant the produced topics are, and how effective the topic model is. How do you get out of a corner when plotting yourself into a corner. If you would like to change your settings or withdraw consent at any time, the link to do so is in our privacy policy accessible from our home page.. The model created is showing better accuracy with LDA. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. using perplexity, log-likelihood and topic coherence measures. # Compute Perplexity print('\nPerplexity: ', lda_model.log_perplexity(corpus)) # a measure of how .