In this post, we discuss techniques to visualize the output and results from topic model based on the gensim package.
In topic modeling with gensim, we followed a structured workflow to build an insightful topic model based on the Latent Dirichlet Allocation algorithm.
In this post, we will build the topic model using gensim’s native LdaModel and explore multiple strategies to effectively visualize the results using matplotlib plots.
To build the LDA topic model using LdaModel(), you need the corpus and the dictionary.
Axis(‘off’) for i, ax in enumerate(axes): if i > 0: corp cur = corp[i-1] topic percs, wordid topics, wordid phivalues = lda model[corp cur] word dominanttopic = , topic ) for wd, topic in wordid topics] ax.
Sentence Coloring of N Sentences def topics per document(model, corpus, start=0, end=1): corpus sel = corpus[start:end] dominant topics =  topic percentages =  for i, corp in enumerate(corpus sel): topic percs, wordid topics, wordid phivalues = model[corp] dominant topic = sorted(topic percs, key = lambda x: x , reverse=True) dominant topics.
Append(topic percs) return(dominant topics, topic percentages) dominant topics, topic percentages = topics per document(model=lda model, corpus=corpus, end=-1) # Distribution of Dominant Topics in Each Document df = pd.
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