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What's the best way to determine the number of topics in the Extract Topics from Data (LDA) operator

cmotencmoten Member Posts: 2 Learner I
I have a dataset made of thousands of ways users have listed product names. For example, Apple MacBook, MacBook, MacBookPro, etc. There are all sorts of products included, but I'm trying to group similar ways people have described them into clusters. The Extract Topics from Data operator seems to be doing the trick but I'm manually having to choose the number of groups. Is there a way to determine the number of groups based on similarity? I hope this makes sense. 

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Answers

  • cmotencmoten Member Posts: 2 Learner I
    Thank you so much for the example. This helps a lot. It looks like you are splitting the text on commas and saving them as columns. You then flip the data around so it lists the columns as rows and renames the last column to “text”. You then append all the individual example sets into one.

    The Optimization Parameter determines that the optimal number of topics is 6, but it seems like the number of topics listed on the Extract Topics from Data operator still shows 10. The results from the Optimization Parameter are being passed through as a parameter for Extract Topics. I think I get how it works.

    I tried applying to my dataset, and initially received an error. I think the overall size was too large, so I took a sample of the data and it worked. The results didn’t get me what I was looking for, but I will have another process to add to my tool belt. I’ll keep experimenting with it. Thanks again for the help.
    lionelderkrikor
  • LaraNeuLaraNeu Member Posts: 4 Learner I
    Hi, I am so happy I found this post as I am I need to find the optimum number of topics for my LDA analysis. Thank you for the process! I ran it on multiple datasets to test it but strangely the result is always 5 topics for any dataset I use. Am I doing anything wrong? Do I have to adjust something in the process besides changing the dataset? Please let me know if you can help. Thanks a lot!
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,078  RM Data Scientist

    somewhat a tricky question. Perplexity gives you a hint where to look, but sometimes you just need to check yourself, because there are sometimes just multiple 'correct solutions'. My prime example is the article i wrote here: https://towardsdatascience.com/topic-mining-on-amazon-reviews-ae76fc286c61 . With low number of topics you have a 'hot beverages' topic. Using more topics it splits into tea and coffee. Both make sense, but you need to decide what you want.

    Metric wise I am a fan of exclusivity because I got better in interpreting it.

    Best,
    Martin

    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • Muhammed_Fatih_Muhammed_Fatih_ Member Posts: 93  Maven
    Hi @lionelderkrikor
    Hi @mschmitz

    first of all thank you for your contributions! That is a very interesting approach! 

    I am interested at the question to which extent additional quality measures can be considered beside Perplexity in RapidMiner in order to ensure a holistic base with regard to the decision of optimal topics? As you mentioned, we have ofentimes not only one and only solution for optimization problems. 

    Thank you in advance for your feedback! 

    Best regards, 

    Fatih
  • Muhammed_Fatih_Muhammed_Fatih_ Member Posts: 93  Maven
    Dear community, 

    somebody who can give feedback on the abovementioned question regarding the evaluation measures for optimal topic determination? 

    Best regards, 

    Fatih
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