How to categorize unlabelled promotional email data I have unlabelled data of promotional emails. I want to categorize those emails based on the topics like fashion, health &amp; wellness, sports, media, Entertainment, etc. Can anyone let me know any effective method or any pre-trained model which i can directly use to categorize those emails (any similar model like YOLO which we have for object detection).
Any kind of help regards to this would be much appreciable. Thanks!
So the question asks what model can you use to put texts into topic categories in a unsupervised way.
The main model that we use for this sort of task is Latent
Dirichelt Allocation (LDA) (https://programmerbackpack.com/latent-dirichlet-allocation-for-topic-modelling-explained-algorithm-and-python-scikit-learn-implementation/)
Also, there is another method you could try, which is to convert texts into document embedding using a Doc2Vec (https://medium.com/wisio/a-gentle-introduction-to-doc2vec-db3e8c0cce5e) model, which will convert the text into an n dimensional vector. From these document embedding you could use any form of clustering (KNN or Agglomerative Clustering) to cluster the texts into topics.