Looking for other opinions on approach to classification problem












1












$begingroup$


I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:




  1. "remove me from your list"

  2. "remove from list"

  3. "please unsubscribe from list"

  4. etc.


All other messages are "good", and should not be removed.



My thoughts on approaches:



I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.



I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.






classification naive-bayes-classifier







share|improve this question









$endgroup$

















    1












    $begingroup$


    I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:




    1. "remove me from your list"

    2. "remove from list"

    3. "please unsubscribe from list"

    4. etc.


    All other messages are "good", and should not be removed.



    My thoughts on approaches:



    I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.



    I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.






    classification naive-bayes-classifier







    share|improve this question









    $endgroup$















      1












      1








      1





      $begingroup$


      I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:




      1. "remove me from your list"

      2. "remove from list"

      3. "please unsubscribe from list"

      4. etc.


      All other messages are "good", and should not be removed.



      My thoughts on approaches:



      I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.



      I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.






      classification naive-bayes-classifier







      share|improve this question









      $endgroup$




      I'm looking to implement an "opt-out" filter for my company. The input is short, text-message style messages. A few examples of opt-out messages are:




      1. "remove me from your list"

      2. "remove from list"

      3. "please unsubscribe from list"

      4. etc.


      All other messages are "good", and should not be removed.



      My thoughts on approaches:



      I was thinking of using a Bayesian classifier here, but not really knowing the solution space (or having much of a background in ML), want to be sure I'm not wasting time on a sub-optimal solution.



      I'm fine with not having the most cutting-edge solution, but want to be sure I'm not missing an approach that might be equally as straightforward but more effective.






      classification naive-bayes-classifier




      classification naive-bayes-classifier






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 9 hours ago









      wheresmycookiewheresmycookie

      1164




      1164






















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          $begingroup$

          You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
          https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html



          If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
          https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/






          share|improve this answer









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            1 Answer
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            $begingroup$

            You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
            https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html



            If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
            https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/






            share|improve this answer









            $endgroup$


















              0












              $begingroup$

              You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
              https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html



              If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
              https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/






              share|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$

                You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
                https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html



                If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
                https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/






                share|improve this answer









                $endgroup$



                You should use text classification techniques. The most basic one is multinomial naive Bayes classifier with tf-idf features. for this method, take a look at this:
                https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html



                If you don’t get enough accuracy (or maybe precision, recall or f-score), you could test more complex techniques e.g. using deep LSTM networks with word embedding. For this method, take a look at this:
                https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 5 hours ago









                pythinkerpythinker

                5431211




                5431211






























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