I am trying to build a model which would take an email message (in English, extracted subject, and body of the email) and identify if it has a question, request or a proposal. Basically, I would like to see the mails that I've not replied but needs a reply. The model can be used as a "filter" in an email client.

What is the best way to go about it?

Related Work:

Parakweet Lab's Email Intent Data Set

Learning to Classify Email into "Speech Acts"

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    $\begingroup$ When you're trying to implement some project or working on a project model,you need to give a snippet of what you're working. In order to get effective feedback from the community.For your information check through the guide lines $\endgroup$ – quintumnia Jul 18 '17 at 16:42
  • $\begingroup$ @quintumnia edited and gave a bit more detail. $\endgroup$ – convergence Jul 19 '17 at 7:36
  • $\begingroup$ Am working on a similar use case. Could you implement? If yes, please share the solution. $\endgroup$ – Nitin Sep 7 '20 at 15:41

This is a categorization problem, not unlike a spam filter. Instead of flagging an email as spam/not-spam, you are flagging whether it has one of the action categories that you have described.

You'll need to start by assembling a training corpus of example email messages and labeling each example to identify which (maybe multiple) of your categories, if any, are actually present in that email.

Next, pre-process that data to extract features for each message. Examples of typical features include word (or n-gram) counts/frequencies (bag of words). As a shortcut, you might to include as a feature a boolean indicating the presence or absence of a particular word or phrase that you suspect will be predictive of one or more categories. Techniques such as stemming can help reduce the number of words/n-grams being used (often increasing accuracy).

Once you have a dataset that consists of features and labels for each training email (possibly breaking this set up into subsets for training, cross-validation, and testing), you'll want to apply a supervised classification algorithm. You might start with linear classifiers such as logistic regression or SVMs, and if you're unsatisfied with the resulting accuracy then you could advance to neural techniques.

  • $\begingroup$ Can you provide more details so that it becomes more easier to implement? $\endgroup$ – DuttaA Mar 18 '18 at 8:31
  • $\begingroup$ "straightforward" ? too optimistic $\endgroup$ – pasaba por aqui Mar 18 '18 at 11:17
  • $\begingroup$ Optimistic perhaps, but all is relative. :-) I'll try to expand a bit more on high level steps. As a note to beginners who want to dig deeper into implementation, Andrew Ng's Coursera course might be a good starting point. $\endgroup$ – G__ Mar 19 '18 at 2:57

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