Approaching a multi-class classification problem but without labels












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I am working on a business problem where I have a movie description dataset. In this dataset I've columns as - Movie title, Movie plot summary, Date of Release. Now based on this information and using machine learning I want to predict which category the movie falls into. For example The Conjuring should fall into Horror and Thriller i.e a multiclass classification problem. Now the problem is I don't have a label column besides the movie description and other info. Now I want my model to predict which categories a movie(unseen to model) should fall into. I have decided 5 labels that I want to consider - Horror, Thriller, Comedy, Romantic and Emotional. So, I want the dataset to look like this -





Conjuring| Description | Title | Horror,Thriller





The notebook| Description| Title | Romantic,Emotional





I believe if I want to proceed this problem as a classification problem then I have to think of some way to create labels to existing dataset by some script and logic. If not supervised then maybe if I can do clustering first and then based on where the data point lies I can do classification later on.



What I have tried ?



Once I decided what my 5 labels should be, I made 50 synonyms for each and then iterated the description of the movies and based on the number of occurrence of words I made frequency and based on majority of the occurrence I decided which category a movie should fall into. Very bad results from this approach.



I used K means clusters from the data and tried to extract information from the clusters. Could not get very meaningful information though.



To be very honest I am pretty clueless and just want a direction how to approach this problem.










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    $begingroup$


    I am working on a business problem where I have a movie description dataset. In this dataset I've columns as - Movie title, Movie plot summary, Date of Release. Now based on this information and using machine learning I want to predict which category the movie falls into. For example The Conjuring should fall into Horror and Thriller i.e a multiclass classification problem. Now the problem is I don't have a label column besides the movie description and other info. Now I want my model to predict which categories a movie(unseen to model) should fall into. I have decided 5 labels that I want to consider - Horror, Thriller, Comedy, Romantic and Emotional. So, I want the dataset to look like this -





    Conjuring| Description | Title | Horror,Thriller





    The notebook| Description| Title | Romantic,Emotional





    I believe if I want to proceed this problem as a classification problem then I have to think of some way to create labels to existing dataset by some script and logic. If not supervised then maybe if I can do clustering first and then based on where the data point lies I can do classification later on.



    What I have tried ?



    Once I decided what my 5 labels should be, I made 50 synonyms for each and then iterated the description of the movies and based on the number of occurrence of words I made frequency and based on majority of the occurrence I decided which category a movie should fall into. Very bad results from this approach.



    I used K means clusters from the data and tried to extract information from the clusters. Could not get very meaningful information though.



    To be very honest I am pretty clueless and just want a direction how to approach this problem.










    share|improve this question









    New contributor




    Pankaj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















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      $begingroup$


      I am working on a business problem where I have a movie description dataset. In this dataset I've columns as - Movie title, Movie plot summary, Date of Release. Now based on this information and using machine learning I want to predict which category the movie falls into. For example The Conjuring should fall into Horror and Thriller i.e a multiclass classification problem. Now the problem is I don't have a label column besides the movie description and other info. Now I want my model to predict which categories a movie(unseen to model) should fall into. I have decided 5 labels that I want to consider - Horror, Thriller, Comedy, Romantic and Emotional. So, I want the dataset to look like this -





      Conjuring| Description | Title | Horror,Thriller





      The notebook| Description| Title | Romantic,Emotional





      I believe if I want to proceed this problem as a classification problem then I have to think of some way to create labels to existing dataset by some script and logic. If not supervised then maybe if I can do clustering first and then based on where the data point lies I can do classification later on.



      What I have tried ?



      Once I decided what my 5 labels should be, I made 50 synonyms for each and then iterated the description of the movies and based on the number of occurrence of words I made frequency and based on majority of the occurrence I decided which category a movie should fall into. Very bad results from this approach.



      I used K means clusters from the data and tried to extract information from the clusters. Could not get very meaningful information though.



      To be very honest I am pretty clueless and just want a direction how to approach this problem.










      share|improve this question









      New contributor




      Pankaj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am working on a business problem where I have a movie description dataset. In this dataset I've columns as - Movie title, Movie plot summary, Date of Release. Now based on this information and using machine learning I want to predict which category the movie falls into. For example The Conjuring should fall into Horror and Thriller i.e a multiclass classification problem. Now the problem is I don't have a label column besides the movie description and other info. Now I want my model to predict which categories a movie(unseen to model) should fall into. I have decided 5 labels that I want to consider - Horror, Thriller, Comedy, Romantic and Emotional. So, I want the dataset to look like this -





      Conjuring| Description | Title | Horror,Thriller





      The notebook| Description| Title | Romantic,Emotional





      I believe if I want to proceed this problem as a classification problem then I have to think of some way to create labels to existing dataset by some script and logic. If not supervised then maybe if I can do clustering first and then based on where the data point lies I can do classification later on.



      What I have tried ?



      Once I decided what my 5 labels should be, I made 50 synonyms for each and then iterated the description of the movies and based on the number of occurrence of words I made frequency and based on majority of the occurrence I decided which category a movie should fall into. Very bad results from this approach.



      I used K means clusters from the data and tried to extract information from the clusters. Could not get very meaningful information though.



      To be very honest I am pretty clueless and just want a direction how to approach this problem.







      machine-learning deep-learning nlp machine-learning-model






      share|improve this question









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      Pankaj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









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      edited 2 mins ago







      Pankaj













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      asked 17 mins ago









      PankajPankaj

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