I have categorized 800,000 documents into 500 categories using the Mahout topic modelling.
Instead of representing the topic using the top 5/10 words for each topics, I want to infer a generic name for the group using any existing algorithm. For the time being, I have used the following algorithm to arrive at the name for the topic:
For each topic
- 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)
Please suggest a approach to arrive at more relevant name for the topics.