The reuters package is a set of reuters articles on 10 different commodities. Here we use the reuters dataset from the textanalysis package as a larger corpus helps to better demonstrate. In the real world you will likely use the map_* functions to run and assess multiple models at once then assess which is best using the perplexity score. The four stage pipeline is basically: Segmentation. Typically, CoherenceModel used for evaluation of topic models. # compute topic coherence model_collection # A tibble: 2 x 3 #> num_topics coherence coherence_model #> #> 1 2 -14.7 #> 2 10 -14.7 This is the implementation of the four stage topic coherence pipeline from the paper Michael Roeder, Andreas Both and Alexander Hinneburg: Exploring the space of topic coherence measures. # create a model collection models ℹ A collection of 2 models. You can also apply the model_coherence to multiple models at once using map_coherence. Topic Coherence is a very important quality measure for our topics. Hence this coherence measure can be used to compare difference topic models based on their human-interpretability. The u_mass and c_v topic coherences capture this wonderfully by giving the interpretability of these topics a number as we can see above. As noted above (Figure 2), automated metrics. The bad_lda_model however fails to decipher between these two topics and comes up with topics which are not clear to a human. in a models mean automated coherence implies a significant improvement in the corresponding human scores.19. This is because, simply, the good LDA model usually comes up with better topics that are more human interpretable. Hence as we can see, the u_mass and c_v coherence for the good LDA model is much more (better) than that for the bad LDA model.
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