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Approach a multi-class classification problem but without labels
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat is the best practice to classify category of named entity in sentenceHow to implement multi class classifier for a set of sentences?Signs there are too many class labelsHow to deal with string labels in multi-class classification with keras?Multi Label Classification for a large number of labelsMulti-task learning for Multi-label classification?Training data for multi-category classification algorithmHow to classify features into two classes without labels?How can I make use of the labels subdivision in a Deep Learning Image classification?Are these Multi-label document classification experiment steps sensible?
$begingroup$
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.
machine-learning deep-learning nlp machine-learning-model
asked 7 mins ago
PankajPankaj
1
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.
$endgroup$
add a comment |
$begingroup$
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.
machine-learning deep-learning nlp machine-learning-model
asked 7 mins ago
PankajPankaj
1
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.
$endgroup$
add a comment |
$begingroup$
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.
machine-learning deep-learning nlp machine-learning-model
asked 7 mins ago
PankajPankaj
1
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
machine-learning deep-learning nlp machine-learning-model
asked 7 mins ago
PankajPankaj
1
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.
asked 7 mins ago
PankajPankaj
1
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.
asked 7 mins ago
PankajPankaj
1
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.
asked 7 mins ago
PankajPankaj
1
asked 7 mins ago
PankajPankaj
1
1
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.
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.
Pankaj is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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