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I have a list/database with short lines of text. Maybe one million such lines. Every line 10-60 characters. I need to categorize them, in the simplest case there would be just two categories, e.g.: "interesting" and "not interesting/spam".

Example

short text category
call me asap interesting
ahahaha spam
... ...
my address is Newstreet 1001 interesting
this is just a dummy spam
it is a good weather today spam
... ...

I need to provide a proof of concept, if this list can be successfully processed with machine learning tools. E.g. in python.

  1. Do I understand it correct that this kind of problems can be solved by classification ML algorithms? Or there are other, better, more promising approaches?

  2. Given, I would like to try the proof of concepts in python, is the sklearn library the tool of choice?

  3. Can somebody provide a link to a "ML classification hello world" example for machine learning newbies? I have found these:

    but... well, maybe there are even shorter and better and even more understandable newbie-examples? If possible, in python.

I know, there were similar questions here in the past like

but they are rather old and I would like to know the state of the art.

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    May 23 at 18:06

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