Viewing resultsΒΆ

Each model supports a few standard outputs for examination of results:

  • List of top N words for each topic

  • Termite plots

  • LDAvis-based plots

    • topik’s LDAvis-based plots use the pyLDAvis module, which is itself a

    Python port of the R_ldavis library. The visualization consists of two linked, interactive views. On the left is a projection of the topics onto a 2-dimensional space, where the area of each circle represents that topic’s relative prevalence in the corpus. The view on the right shows details of the composition of the topic (if any) selected in the left view. The slider at the top of the right view adjusts the relevance metric used when ranking the words in a topic. A value of 1 on the slider will rank terms by their probabilities for that topic (the red bar), whereas a value of 0 will rank them by their probabilities for that topic divided by their probabilities for the overall corpus (red divided by blue).

Example syntax for these:

>>> model.get_top_words(topn=10)

>>> from topik.viz import Termite
>>> termite = Termite(lda.termite_data(n_topics), "Termite Plot")
>>> termite.plot(os.path.join(dir_path, 'termite.html'))

View Termite Plot

>>> from topik.viz import LDAvis
>>> raw_data = read_input("reviews", content_field=None)
>>> tokenized_corpus = raw_data.tokenize()
>>> n_topics = 10
>>> model = registered_models["LDA"](tokenized_corpus, n_topics)
>>> from topik.viz import plot_lda_vis
>>> plot_lda_vis(model.to_py_lda_vis())

View LDAVis Plot

Each model is free to implement its own additional outputs - check the class members for what might be available.