How do I give my name to topics using LDA?
How to give name to topics created using LDA?
- Take all the documents belonging to the topic (using the document-topic distribution output)
- Run python nltk to get the noun phrases.
- Create the TF file from the output.
- name for the topic is the phrase (limited towards max 5 words)
What are topics in LDA?
Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions. Here we are going to apply LDA to a set of documents and split them into topics.
How to label topics automatically after applying LDA?
Most creators of topic models assign the labels to the topics manually. Automated labeling is perhaps possible for English language (with lots of rich language resources around), for some hints see these question on other Stackexchange sites and their answers: How to give name to topics created using LDA?
How is LDA presented as mixture of keywords with their weights?
I suppose it is late to answer, but it may turn out to be helpful for others. LDA presents all topics as a mixture of keywords with their weights For example, in gensim implementation it looks like this:
How to calculate the weight of a topic in LDA?
LDA presents all topics as a mixture of keywords with their weights For example, in gensim implementation it looks like this: topic #0: 0.009*river + 0.008*lake + 0.006*island + 0.005*mountain + 0.004*area + 0.004*park + 0.004*antarctic + 0.004*south + 0.004*mountains + 0.004*dam
How to generate a list from Wikipedia titles?
A few ideas you’ll often see.. Generate a list from Wikipedia titles, extract keyphrases, predict the related wikipedia pages and use the keyphrases. Generate a hand-labeled dataset.