I am currently working on a project where my job is to intent analysis of an article. Suppose i am given with the article and i want to classify that what kind of article it is?. Any suggestions, Which kind of model would be best to apply?
I think the easiest way would be to cluster them with wordvectors. The "20 Newspaper" dataset would be a good test for the algorithm http://qwone.com/~jason/20Newsgroups/
On the Sklearn Website, you will find some easy example for using wordvectors and classification: http://scikit-learn.org/stable/datasets/twenty_newsgroups.html
If you go deeper into this space you could start using LSTM networks and neural networks, but this is a little bit harder to develop. So first, try to use the problem statistically. If you want to analyze the text semantically you will need LSTM.
I hope I could help you.
It sounds like you are trying to classify each article based on a set of tags that are inherently generated from each of the articles.
As an alternative to Christian's solution (which I think does serve the purpose that you had described), you could consider using an N-Gram model and Parts-of-Speech (POS) tagging in order to do the following (in this order):
- Calculate the frequency of recurring, common phrases within a document / corpus
- Apply POS tagging to classify nouns (proper and common), verbs, adjectives... etc.
- (Optional) Apply some sort of rule to only select the top Y most frequent terms
Using a standard language model, you can compute the relative probabilities of those words occurring within each document to then weight which article is most likely to mention a certain tag.